Barely three months into the new year and we are happy to announce a monumental milestone reached - 150 million downloads.
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This achievement solidifies IntechOpen’s place as a pioneer in Open Access publishing and the home to some of the most relevant scientific research available through Open Access.
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We are so proud to have worked with so many bright minds throughout the years who have helped us spread knowledge through the power of Open Access and we look forward to continuing to support some of the greatest thinkers of our day.
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Thank you for making IntechOpen your place of learning, sharing, and discovery, and here’s to 150 million more!
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The book comprises single chapters authored by various researchers and is edited by a group of experts active in the physical sciences, engineering, and technology research areas. All chapters are complete in themselves but united under a common research study topic. This publication aims at providing a thorough overview of the latest research efforts by international authors on physical sciences, engineering, and technology, and opens new possible research paths for further novel developments.",isbn:"978-1-78984-126-8",printIsbn:"978-1-78984-125-1",pdfIsbn:"978-1-78923-899-0",doi:"10.5772/intechopen.77422",price:119,priceEur:129,priceUsd:155,slug:"solar-cells",numberOfPages:168,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"0a247e3e90115e9dce4f44a6996bc866",bookSignature:"Majid Nayeripour, Mahdi Mansouri and Eberhard Waffenschmidt",publishedDate:"March 25th 2020",coverURL:"https://cdn.intechopen.com/books/images_new/7619.jpg",numberOfDownloads:7157,numberOfWosCitations:4,numberOfCrossrefCitations:5,numberOfCrossrefCitationsByBook:0,numberOfDimensionsCitations:13,numberOfDimensionsCitationsByBook:0,hasAltmetrics:0,numberOfTotalCitations:22,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"September 13th 2018",dateEndSecondStepPublish:"October 4th 2018",dateEndThirdStepPublish:"December 3rd 2018",dateEndFourthStepPublish:"February 21st 2019",dateEndFifthStepPublish:"April 22nd 2019",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6,7",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"66929",title:"Prof.",name:"Majid",middleName:null,surname:"Nayeripour",slug:"majid-nayeripour",fullName:"Majid Nayeripour",profilePictureURL:"https://mts.intechopen.com/storage/users/66929/images/system/66929.png",biography:"After 8 years of industrial experience and academic work in the electrical engineering and renewable energy fields, Prof. Majid Nayeripour was promoted to full professor in the field of micro-grids in 2016. He was given a sabbatical from Shiraz University of Technology, Iran, and was invited to Cologne University of Applied Sciences, Germany in January 2016. During his research, he gained new experiences about problems relating to high penetration levels of distributed generations and toward having 100% renewable energy in Germany, and as a result, he was awarded a fellowship program for an experienced researcher from the Alexander von Humboldt (AvH) Foundation in 2017. Currently, he is at the Cologne University of Applied Science and is involved in research on the control and dynamic investigation of interconnected micro-grids. He has published more than 120 journals and conference papers, five books, and supervised more than ten research projects.",institutionString:"Cologne University of Applied Sciences",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"5",institution:null}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:{id:"291302",title:"Dr.",name:"Mahdi",middleName:null,surname:"Mansouri",slug:"mahdi-mansouri",fullName:"Mahdi Mansouri",profilePictureURL:"https://mts.intechopen.com/storage/users/291302/images/system/291302.png",biography:"Dr. Mahdi Mansouri was born in Yazd, Iran in 1975. He received both his B.S. degree in electronic engineering and MSc degree in electronic power from the Sharif University of Technology at Power Electronics-STATCOM and his Ph.D. degree in renewable energy systems from doubly fed induction generator (DFIG)-based wind turbines from the Shiraz University of Technology. He has 20 years of experience in high-voltage transmission substations and lines as a technical engineer, a consultant, and an executive project manager. His research interests include flexible alternating current transmission system devices, power quality, and power system protection. He currently conducts power electronics, power-relay protection, and power-quality projects as a consultant and project manager.",institutionString:"Yazd University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:null},coeditorTwo:{id:"264283",title:"Dr.",name:"Eberhard",middleName:null,surname:"Waffenschmidt",slug:"eberhard-waffenschmidt",fullName:"Eberhard Waffenschmidt",profilePictureURL:"https://mts.intechopen.com/storage/users/264283/images/system/264283.jpg",biography:"Eberhard Waffenschmidt (Prof. Dr. Ing.) received a degree in electronic engineering and Ph.D. degree at RWTH Technical University, Aachen, Germany. From 1995 to 2011, he was employed at Philips Research, Aachen, Germany, as a senior scientist. Since 2011, he is a Professor of Electrical Power Grids at TH Köln – University of Applied Sciences, Cologne, Germany. There, he participates in the Cologne-Institute for Renewable Energy (CIRE). He is an IEEE-member since 2005, meanwhile becoming a Senior Member. He is currently a Chairman of the Solarenergie-Förderverein Deutschland e.V. (SFV, Society to Promote Solar Energy Germany), Aachen, Germany. His current research interests include identifying and removing obstacles on the way to a 100% use of renewable energy.",institutionString:"TH Köln – University of Applied Sciences",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"TH Köln – University of Applied Sciences",institutionURL:null,country:{name:"Germany"}}},coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"740",title:"Electronic Devices and Materials",slug:"electronic-devices-and-materials"}],chapters:[{id:"67140",title:"Industrial Silicon Solar Cells",doi:"10.5772/intechopen.84817",slug:"industrial-silicon-solar-cells",totalDownloads:1645,totalCrossrefCites:0,totalDimensionsCites:2,hasAltmetrics:1,abstract:"The chapter will introduce industrial silicon solar cell manufacturing technologies with its current status. Commercial p-type and high efficiency n-type solar cell structures will be discussed and compared so that the reader can get a head-start in industrial solar cells. A brief over-view of various process steps from texturing to screen-printed metallization is presented. Texturing processes for mono-crystalline and multi-crystalline silicon wafers have been reviewed with the latest processes. An over-view of the thermal processes of diffusion and anti-reflective coating deposition has been presented. The well-established screen-printing process for solar cell metallization is introduced with the fast-firing step for sintering of the contacts. I-V testing of solar cells with various parameters for solar cell characterization is introduced. Latest developments in various processes and equipment manufacturing are also discussed along with the expected future trends.",signatures:"Mehul C. Raval and Sukumar Madugula Reddy",downloadPdfUrl:"/chapter/pdf-download/67140",previewPdfUrl:"/chapter/pdf-preview/67140",authors:[{id:"279539",title:"Dr.",name:"Mehul C.",surname:"Raval",slug:"mehul-c.-raval",fullName:"Mehul C. Raval"},{id:"289613",title:"MSc.",name:"Sukumar",surname:"Madugula Reddy",slug:"sukumar-madugula-reddy",fullName:"Sukumar Madugula Reddy"}],corrections:null},{id:"67927",title:"Review on Metallization in Crystalline Silicon Solar Cells",doi:"10.5772/intechopen.84820",slug:"review-on-metallization-in-crystalline-silicon-solar-cells",totalDownloads:1648,totalCrossrefCites:4,totalDimensionsCites:9,hasAltmetrics:0,abstract:"Solar cell market is led by silicon photovoltaics and holds around 92% of the total market. Silicon solar cell fabrication process involves several critical steps which affects cell efficiency to large extent. This includes surface texturization, diffusion, antireflective coatings, and contact metallization. Among the critical processes, metallization is more significant. By optimizing contact metallization, electrical and optical losses of the solar cells can be reduced or controlled. Conventional and advanced silicon solar cell processes are discussed briefly. Subsequently, different metallization technologies used for front contacts in conventional silicon solar cells such as screen printing and nickel/copper plating are reviewed in detail. Rear metallization is important to improve efficiency in passivated emitter rear contact cells and interdigitated back contact cells. Current models on local Al contact formation in passivated emitter rear contact (PERC) cells are reviewed, and the influence of process parameters on the formation of local Al contacts is discussed. Also, the contact mechanism and the influence of metal contacts in interdigitated back contact (IBC) cells are reviewed briefly. The research highlights on metallization of conventional screen printed solar cells are compared with PERC and IBC cells.",signatures:"Nagarajan Balaji, Mehul C. Raval and S. Saravanan",downloadPdfUrl:"/chapter/pdf-download/67927",previewPdfUrl:"/chapter/pdf-preview/67927",authors:[{id:"279539",title:"Dr.",name:"Mehul C.",surname:"Raval",slug:"mehul-c.-raval",fullName:"Mehul C. Raval"},{id:"6708",title:"Dr.",name:"S.",surname:"Saravanan",slug:"s.-saravanan",fullName:"S. Saravanan"},{id:"279759",title:"Mr.",name:"Nagarajan",surname:"Balaji",slug:"nagarajan-balaji",fullName:"Nagarajan Balaji"}],corrections:null},{id:"66273",title:"Polymers in Solar Cells",doi:"10.5772/intechopen.85312",slug:"polymers-in-solar-cells",totalDownloads:771,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Due to the promising properties of semiconducting polymers they have attracted wide spread interest. During the last decade we have worked on a smart chemistry approach to these substrates and organometallic compounds with special focus on design, synthesis and characterization of materials that could be used in photovoltaic devices. In these materials, one of the most important aspects is high absorbance in the UV-Vis spectrum as a necessary characteristic for high conversion rates of light to electric energy. We were able to show that this can be achieved introducing certain functional groups as, e.g., NO2-moieties. Another important aspect is the interaction with the buffer layers that also form part of the whole photovoltaic device. Here we show that a double layer of CuI/MoO3 increased the energy yield for a large variety of organic substrates along with related results from other leading groups reported in literature.",signatures:"Pedro Pablo Zamora and Klaus Bieger",downloadPdfUrl:"/chapter/pdf-download/66273",previewPdfUrl:"/chapter/pdf-preview/66273",authors:[{id:"277271",title:"Dr.",name:"Pedro Pablo",surname:"Zamora",slug:"pedro-pablo-zamora",fullName:"Pedro Pablo Zamora"},{id:"279694",title:"Dr.",name:"Klaus",surname:"Bieger",slug:"klaus-bieger",fullName:"Klaus Bieger"}],corrections:null},{id:"66902",title:"Impact of Active Layer Morphology, Density of States, Charge Carrier Concentration, and Local Charge Density Fluctuations on Bimolecular Recombination of Bulk Heterojunction Solar Cells: A Theoretical Perspective",doi:"10.5772/intechopen.85074",slug:"impact-of-active-layer-morphology-density-of-states-charge-carrier-concentration-and-local-charge-de",totalDownloads:697,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"We study the merits of a reaction-diffusion model to unravel the effects of active layer morphology and donor-acceptor interfacial roughness, density of states, charge carrier concentration, and local charge density fluctuations on the bimolecular recombination kinetics in bulk heterojunction organic semiconductors. We consider the cases of a single and composite electronic density of states (DoS) that consists of a superposition of a Gaussian and an exponential DoS. Using kinetic Monte Carlo (KMC) simulations, we apply the reaction-diffusion model in order to investigate the factors impacting bimolecular recombination (BMR) kinetics and rates at short and long time scales. We find that morphology, donor-acceptor interfacial roughness, and charge carrier concentration only affect BMR time, whereas DoS characteristics as well as local charge density fluctuations can significantly impact BMR kinetics and rates.",signatures:"Daniel Christiansen and Shafigh Mehraeen",downloadPdfUrl:"/chapter/pdf-download/66902",previewPdfUrl:"/chapter/pdf-preview/66902",authors:[{id:"280108",title:"Associate Prof.",name:"Shafigh",surname:"Mehraeen",slug:"shafigh-mehraeen",fullName:"Shafigh Mehraeen"},{id:"295965",title:"Ph.D.",name:"Daniel",surname:"Christiansen",slug:"daniel-christiansen",fullName:"Daniel Christiansen"}],corrections:null},{id:"69851",title:"Nanoplasmonic for Solar Energy Conversion Devices",doi:"10.5772/intechopen.84953",slug:"nanoplasmonic-for-solar-energy-conversion-devices",totalDownloads:832,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"The effect of nanoplasmonic (Ag) on the performance of DSSCs has been studied in doped and undoped ZnO (DZ and UZ) NPs, which were prepared by the urea-assisted combustion route. Different techniques were conducted to characterize DZ and UZ NPs. XRD patterns were indexed to the hexagonal wurtzite structure of ZnO NPs (ICSD-52362). The values of average crystalline size of UZ and DZ (1.0 mol% Ag) NPs were 20.45 and 22.30 nm, respectively. HR-TEM micrograph revealed good crystallization with an intermediate or poor agglomeration with distribution of semispherical morphologies of ZnO NPs. The energy bandgap of UZ and DZ NPs was changed from 3.21 to 3.31 eV. The deconvolution of the PL spectra recognized eight peaks into near ultraviolet (NUV) and visible regions. The PL emission of visible region overshadowed NUV transition. The photovoltaic cell with the doped photoanode DZ:1.0 mol% Ag exhibited the best performance parameters: Voc = 0.46 V, Jsc = 7.81 mA.cm−2, Pm = 1.91, FF = 51%, and η = 1.91%. A double exponential function was used as a powerful fitting function for the TOCVD data. The results revealed that τn in the UZ NPs photoanode was longer than that in the DZ:1.0 mol% Ag NPs photoanode.",signatures:"Samy K.K. Shaat, Hussam Musleh, Jihad Asad, Nabil Shurrab, Ahmed Issa, Amal AlKahlout and Naji Al Dahoudi",downloadPdfUrl:"/chapter/pdf-download/69851",previewPdfUrl:"/chapter/pdf-preview/69851",authors:[{id:"242721",title:"Prof.",name:"Naji",surname:"Al Dahoudi",slug:"naji-al-dahoudi",fullName:"Naji Al Dahoudi"},{id:"277780",title:"Dr.",name:"Samy K. K.",surname:"Shaat",slug:"samy-k.-k.-shaat",fullName:"Samy K. K. Shaat"},{id:"290502",title:"Dr.",name:"Hussam",surname:"Musleh",slug:"hussam-musleh",fullName:"Hussam Musleh"},{id:"290503",title:"Mr.",name:"Jihad",surname:"Asad",slug:"jihad-asad",fullName:"Jihad Asad"},{id:"290504",title:"Dr.",name:"Nabil",surname:"Shurrab",slug:"nabil-shurrab",fullName:"Nabil Shurrab"},{id:"290505",title:"Dr.",name:"Ahmed",surname:"Issa",slug:"ahmed-issa",fullName:"Ahmed Issa"},{id:"290506",title:"Prof.",name:"Amal",surname:"AlKahlout",slug:"amal-alkahlout",fullName:"Amal AlKahlout"}],corrections:null},{id:"65905",title:"Heterojunction-Based Hybrid Silicon Nanowires Solar Cell",doi:"10.5772/intechopen.84794",slug:"heterojunction-based-hybrid-silicon-nanowires-solar-cell",totalDownloads:924,totalCrossrefCites:0,totalDimensionsCites:1,hasAltmetrics:0,abstract:"It is known that defect-free, i.e., oxide-free, Si nanowires (Si NWs) exhibit lower defect density emissions than unmodified Si NWs. This is successfully established by grafting organic molecules on the surface. Here we show that by using a two-step chlorination/alkylation process, we are able to graft organic molecules on Si NWs for solar cell applications. Afterward, we show the electronic properties of the molecular surface (such as work function and band bending). Finally, we correlate these properties to the solar cell performance.",signatures:"Riam Abu Much, Prakash Natarajan, Awad Shalabny, Sumesh Sadhujan, Sherina Harilal and Muhammad Y. Bashouti",downloadPdfUrl:"/chapter/pdf-download/65905",previewPdfUrl:"/chapter/pdf-preview/65905",authors:[{id:"274866",title:"Prof.",name:"Muhammad Y.",surname:"Bashouti",slug:"muhammad-y.-bashouti",fullName:"Muhammad Y. 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Recent developments have suggested the incorporation of aqueous-mediated synthesis for the QDs, as it is greener, environment friendly, cost-effective and reproducible. However, the process involves several challenges, which ought to be met in order to produce stable, consistent and sustainable product formation. The present review discusses the significance of semiconducting QDs, their synthesis through various processes, their pros and cons, and above all the advantage of aqueous-mediated, atom economic and energy-saving methodologies.",signatures:"Jejiron Maheswari Baruah and Jyoti Narayan",downloadPdfUrl:"/chapter/pdf-download/65027",previewPdfUrl:"/chapter/pdf-preview/65027",authors:[{id:"226186",title:"Dr.",name:"Jyoti",surname:"Narayan",slug:"jyoti-narayan",fullName:"Jyoti Narayan"},{id:"237249",title:"Ph.D.",name:"Jejiron Maheswari",surname:"Baruah",slug:"jejiron-maheswari-baruah",fullName:"Jejiron Maheswari Baruah"}],corrections:null}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},subseries:null,tags:null},relatedBooks:[{type:"book",id:"2023",title:"Sustainable Growth and Applications in Renewable Energy 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1. Introduction
Since the mid-twentieth century, Dynamic Programming (DP) has proved to be a flexible and powerful approach to address optimal decisions problems. Nevertheless, a decisive drawback of the conventional DP is the need for exploring the whole state space in order to find the optimal solution. The immense amount of mathematical operations involved to solve real-scale problems, constrained the application of DP to small or highly simplified cases. Indeed, state space grows exponentially with the number of variables when considering multivariate optimization. The curse of dimensionality is a well-known limitation of conventional DP algorithms for tackling large-scale problems ubiquitous in real science and engineering applications.
In the last decades, many new algorithms emerged in different branches of science to overcome the inherent limitations of conventional DP. Unlike conventional DP, these algorithms avoid enumerating and calculating every possible state of a system during the optimization process. Instead, they estimate relevant features of the state space. This approach circumvents the dimensionality limitations of the conventional DP while retaining many of its advantages.
In this chapter, the application of advanced stochastic dynamic programming techniques to the optimization of the forward sell strategy of a power generator subjected to delivery risk is considered. The proposed approach allows rebalancing the portfolio during the period of analysis. In electricity markets, a power generator can sell in advance part or all its future energy production at a fixed price, hedging against the high price volatility of the spot market. The strategy of eliminating the price risk by selling in advance the entire production in the forward market to a fixed price is often thought as the minimum-risk trading policy. Nonetheless, it can be proven that this is not the case for most generators. The outages of the generation units and transmission lines, as well as unforeseen limitations in the primary energy supply expose generators to delivery risk [1]. Delivery risk considerably modifies the probability distribution of profits, shifting the optimal trading strategy toward a portfolio mixing forward contracts and power sold in the spot market. Because of the size of the probability state space and the limited computing capabilities, the problem of the optimal trading strategy has not a closed form solution and thus, its determination is matter of current study. The increase in computing power and recent developments in Operational Research has brought new insights into the solution of such problems.
In the past decade and by virtue of the ever increasing computational power, many methods emerged in different scientific fields with several different names: Reinforced Learning, Q-Learning, Neuro-Dynamic Programming, etc. All these methods were later brought together in what is currently known as Approximated Dynamic Programming (ADP) [2],[3]. These algorithms resign the exhaustive enumeration and calculation of the space-state typically performed by conventional DP. Instead, they iteratively approximate a function of the space state through stochastic simulation and statistical regression techniques, circumventing the dimensionality problem of DP.
Although ADP algorithms are being used in several other fields of science, the application to design optimal trading strategies in power markets has not been proposed so far. In this chapter, ADP techniques are exploited to optimize the selling strategy of a power generator trading in a frictional market with transaction costs. Three available products are considered: selling in the spot market, and/or get involved in quarterly and one-year forward contracts. The objective of the generator is to maximize the expected profit while limiting financial risk. Decisions can be made only at the beginning of each month. At each decision stage, the current trading position can be changed at a cost in order to rebalance the portfolio.
2. Approximate dynamic programming
In the field of decision making, it is often useful to assess what could be expected from each possible decision given the information available. After evaluating the outcome of each alternative decision, a simple comparison is enough to take the optimal course of action. This approach is straightforward but also naive. Real problems often present simply too many possible options to evaluate. Moreover, if the problem involves sequential decision stages, the number of possible solution paths scales up exponentially. Finally, outcomes are frequently subjected to uncertainty. So, several outcomes could present themselves for each decision, augmenting further the size of the problem.
Therefore, in order to keep the size of the problem within reach, shortcuts and simplifications areoften necessary. Dynamic Programming (DP) is a clever way to reduce the number of options based on Bellman´s Principle of Optimality: “An optimal policy has the property that whatever the initial state and initial decision are, the remaining decisions must constitute an optimal policy with regard to the state resulting from the first decision” [4]. This leads to the fact that from any given state there is an optimal decision trajectory that once solved can be used for every other path containing that state. Therefore for each state is only necessary to hold the information of the best solution until the end, ruling out suboptimal options. This rule prevents the exponential growth in the decision sequence path, scaling the problem only linearly.
Yet, other reasons of explosive dimensionality growth remain, namely the number of states, as well as decision and outcome spaces. For small financial decision problems, conventional DP algorithms are able to find the optimal policy. For real-scale problems, gross simplifications are often necessary to keep tractability. Sometimes, these simplifications render the model unrealistic turning results meaningless.
Finding an appropriate combination of financial instruments in a portfolio can be portrayed as a Markov Decision Problem (MDP). A MDP is defined as a succession of states that are reached through decisions (actions) followed by an outcome (reaction) of the system. After each decision, the system evolves into another state, according probabilities defined by the previous state and the decision taken. Each transition between states has a cost, usually dependent on the action taken, and a reward produced by the reaction of the system, as depicted in Figure 1. In these processes, a decision maker should choose a sequence of actions so that the expected sum of rewards minus the sum of incurred costs is maximized over a certain period of time.
Figure 1.
Markov Decision Process depiction
According to the Bellman´s Principle of Optimality, for every MDP can be determined a series of Value Functions, which represents the continuation value of each state. The continuation value associated to a given state is the expected sum of rewards that the optimal strategy would yield from that state until the end of the process (or the expected average reward if the MDP is infinite).
It is easy to see how the value functions of a MDP can be found using a classic backwards DP algorithm. Starting from the final states, a DP algorithm exhaustively calculates the continuation value for a discrete number of states. All these continuation values, collected in a lookup table, constitute later the Value Functions which are accurate but not very compact or easy to calculate. After acquiring the Value Functions, it is simple to find an optimal decision for each state as the one that maximizes the sum of the expected reward and the expected continuation value of the next state or states. However, the problems that a DP algorithm can address by this procedure are restricted by the size of their state spaces.
Other forms to represent and/or approximate the Value Functions can then be proposed. These approaches should not require exhaustive calculation of every state. The Value Functions can be interpolated between computed states. Approximate Dynamic Programming algorithms are built on this cornerstone: approximation of the Value Functions in the state space domain. The estimation methods can be linear regressions, artificial neural networks, etc. Several authors make detailed analysis of MDPs and the use of ADP and DP algorithms to solve them [2],[3].
For approximating and updating the Value Functions, the proposed algorithm uses linear regression on Gaussian radial basis functions jointly with Monte Carlo simulations to consider randomness. An interior-point optimization algorithm is implemented to make decisions.
The ADP algorithm starts with a series of approximations of the value functions, usually constant. Then taking a Monte Carlo sample, a simulation of the system is conducted. At each decision stage, the algorithm makes a decision that is optimal regarding to the current state and the available approximations. Finally, after each Monte Carlo simulation, decisions and outcomes are used to refine the estimation of the Value Functions and complementary Risk Functions, denoted by Vt and Rt respectively. The process continues iteratively, until a certain termination criterion is fulfilled. A simple diagram of this approach is illustrated in Figure 2.
Figure 2.
Simulation and estimation update as performed by the proposed ADP algorithm
2.1. Regression technique
An approximation of a system involves finding a function that can predict the output values for a given set of inputs. In this case, the inputs are the state variables and the decision and the outputs are the continuation value and risk.
To approximate the value and risk functions one could use several methods such as linear regressions, artificial neural networks, splines, etc. In the context of electricity trading, it will be discussed how to use linear regression with radial basis functions. The same principles and techniques apply to any regression based on linear parameters.
The main feature of the algorithm is that the approximation is used to make decisions while collecting new data to further improve the currently available approximation. Thus, it is necessary that the regressed function fitting the data can be readily updated as new data is simulated, making the improved approximations immediately available for the next simulation. To fulfill this requirement, a recursive regression technique can be used.
Let be a set of inputs X and a set of collected data from outputs Y\n\t\t\t\t
The matrix of inputs X has n data points and g dimensions and the matrix of outputs Y has n data points and h dimensions. Then, the approximation function is:
Where the kernel functions φ(X) transform the input variables X into a k-dimensional space and are in general non-linear. Here, it is important to notice that despite the fact the φ functions are nonlinear, the approximation is still linear with respect the parameters θ. Therefore, the regression parameters can easily be found by solving a linear system of equations.
The parameter vector θ that minimizes the mean quadratic error MQE of the estimated outputs is:
\n\t\t\t\t
θ=[ZTZ]−1ZTY=AZTYE4
\n\t\t\t\t
A=[ZTZ]−1E5
\n\t\t\t\t
The dimension of matrices X, Y and Z increase as new points are simulated, and thus the estimated parameters change as new data is collected. On the other hand, the algorithm needs the approximation to get new data. One approach could be calculating the new parameters after a number of simulations with equation (4) but it soon reveals impractical as an increasing amount of data need to be stored. Instead, by storing only the matrix A and the parameter vector θ, the matrix itself can recursively be updated jointly with the parameters, following the process described in [2]:
In the i-th iteration, a new data point xi,yi is simulated and the matrices X and Y become:
Finally, canceling out we get a formula to update the parameter vector:
\n\t\t\t\t
θi=θi−1−ρiAi−1ziT[ziθi−1−yi]E19
\n\t\t\t\t
Note that in order to update the approximations, it is needed to store and update only the values of the matrix A and the parameter vector θ. The complete set of update formulas is then:
\n\t\t\t\t
ρi=11+ziAi−1ziTE20
\n\t\t\t\t
θi=θi−1−ρiAi−1ziT[ziθi−1−yi]E21
\n\t\t\t\t
Ai=Ai−1−ρiAi−1ziTziAi−1E22
\n\t\t\t\t
By using the equations (20), (21) and (22), it is possible to update a regression while using it to take decisions within the ADP algorithm without explicitly storing the entire dataset and performing the matrix inversion after each Monte Carlo simulation. However, an important issue arises at the start of the recursive process, namely the starting values for both the parameter vector θ and the matrix A. One possibility is to carry first a number of simulations with random decisions, and then use the data collected to calculate the starting values for θ and A. Another option is to use a diagonal matrix:
\n\t\t\t\t
Ai=[a1,10…00a2,2…0⋮⋮⋱⋮00…ak,k]E23
\n\t\t\t\t
This option is usually simpler despite the fact that setting the absolute values of the elements requires care and it is not always clear in the literature. To set suitable values, a closer look at the regression formulas for matrix A is needed:
The matrix Ai is the inverse of a matrix sum whose diagonal elements are positive and increasing with i. That means that the absolute value of the diagonal elements of A would in general decrease as i increases. The relation factor between the starting value of all the elements aj,j and the corresponding expected value of (1/zp,j)2 controls how new values modify the parameter vector θ.
\n\t\t\t\t
aj,j=δ⋅E[1/(zp,j)2]E28
\n\t\t\t\t
Setting starting diagonal elements with a small δ value implies implies a large number of iterations i. This would mean that the starting parameter vector represents already a large amount of “fictional” data. In that case, any new real data sample will have little impact on the parameter vector, and the convergence rate of the approximation might be significantly slowed. On the other hand, setting initial diagonal values calculated with large δ enables the algorithm to large modifications on the parameter vector as new data is collected, which could cause instability of the parameter vector and in the decision computed, again reducing the overall convergence rate. In practice, the factor δ depends on the problem and it must be assessed accordingly. From extensive experimentation, values of δ between 1 and 1000 tend to work most of the time.
For initializing the parameter vector θ0, it is usual to set a constant vector. Values obtained from the formula Y^s=φ(E(X))⋅θ0 produce coherent results. It is important to notice that the starting values of the parameter vector are important in cases where δ is small, while for a large δ the starting vector value is lost in the first iterations.
2.2. Kernel functions
The approximations are highly influenced by the type and parameterization of the kernel functions. These functions transform the original inputs xm,1≤m≤g into a k-dimensional space where a linear correlation between outputs and transformed inputs is more accurate.
There are several kernel functions, such as polynomial, trigonometric, logarithmic, radial basis, etc. The algorithm proposed in this work uses Gaussian radial basis functions, whose general formula is:
\n\t\t\t\t
φj(x)=e−(α‖x−cj‖)2E29
\n\t\t\t\t
where cj is 1xg vector called a center or centroid, with g being the number of input variables.
The functions measure the distance of each variable in the input state space to k centers and then transform each distance using a Gaussian function. The Euclidean norm is typically used to measure the distance. Nevertheless, any other norm can be considered.
The number and relative position of centers cj are important. The number of centers determines the dimension of the matrices in the linear regression, and also the smoothing and over-fitting properties of the approximation. There must be enough centers and they must be placed to “cover” the entire state space, avoiding blanks in regions of interest while keeping the number of centers to a minimum. A random setting of the centers in the state space can serve in some cases. However, it is usually more appropriate to place the centers using techniques such as Latin Hyper Cube Sampling, as it is done in the implemented algorithm.
2.3. ADP and linear regressions in the context of distributed computing
In the context of ADP, a large amount of the calculation is attributed to the Monte Carlo simulations. Consequently, distributed computing techniques can be exploited to dramatically improve and speed up the optimization. The optimal point between thread opening and result gathering basically depends on the hardware system, network latency and simulation times. With time-consuming simulations, it is often useful to parallelize the program after each approximation update, leaving several threads gather more data in parallel before any update. But if the simulation time is short, the overhead times to distribute the calculation are usually larger than the speed up achieved through parallel simulation. In such cases, another approach should be used. A practical approach is to run parallel ADP algorithms for the same problem in several threads in order to combine and synchronize all approximations at regular time intervals. The combination of the results of two or more independent threads is based on the same mathematical basis as the recursive update, provided that the non-linear parametric functions are identical for all threads.
As shown in equation (4), the optimal parameter vector is:
\n\t\t\t\t
θ=[ZTZ]−1ZTY=[∑i=1nziTzi]−1⋅[∑i=1nziTyi]E30
θ=[B]−1C,B=∑i=1nziTzi,C=∑i=1nziTyiE31
\n\t\t\t\t
where each zi and yi represent the transformed inputs and the outputs respectively of the i-th simulation of a total of n data points. As i increases, the matrices B and C summarize all the regression data collected. The data simulated by several threads of ADP can be easily combined using these matrices. As parallel ADP threads a and b gather different sets of data, the parameter vectors that they approximate and update are different
\n\t\t\t\t
θa=[Ba]−1Ca=[∑i=1vziTzi]−1⋅[∑i=1vziTyi]E32
\n\t\t\t\t
θb=[Bb]−1Cb=[∑i=vnziTzi]−1⋅[∑i=vnziTyi]E33
\n\t\t\t\t
However, saving the matrices B and C allows to recombine all the data later as
This result can be generalized to any number of parallel threads.
In general, data gathered from several parallel algorithms can be recombined by updating and saving the matrices B and C after each simulation i of each thread j. The update formulas are:
\n\t\t\t\t
Bij=Bi-1j+ziTziE36
\n\t\t\t\t
Cij=Ci−1j+ziTyiE37
\n\t\t\t\t
After a fixed simulation time, they can be synchronized and recombined, so all the threads share the same data gathered:
\n\t\t\t\t
B=∑j=1thBjE38
\n\t\t\t\t
C=∑j=1thCjE39
\n\t\t\t\t
Finally, each thread can restart the simulation process using as starting values for θ and A\n\t\t\t\t
\n\t\t\t\t
A=[B]−1E40
\n\t\t\t\t
θ=[B]−1CE41
\n\t\t\t\t
The final approximation is not as good as the one carried out by a single thread for the same amount of simulations. This is due to the fact that the decisions taken by the single thread algorithm have always all the information gathered up to the decision point while the multithread algorithm lacks the information gathered by other threads since the last synchronization. Nevertheless, with the correct choice of synchronization and simulation cycles, the overall optimization process can run much faster.
3. Electricity trading
3.1. Electricity markets
Since about two decades, the power industry had undergone a major restructuring in many countries. The former vertically-owned and centrally-planned electricity utilities have been unbundled in separate and independent business segments: the generation, transmission and distribution sectors. Unlike the latter two segments, which have remained as natural monopolies under regulation, power generation is now a business subject to competition in the open marketplace [5].
Electricity is a commodity with some very distinctive features. First, modern societies are exceedingly dependent on a continuous delivery of electrical power, placing a very high value to supply reliability. Because electrical energy cannot be economically stored in considerable amounts, production and consumption must be continuously in perfect balance. In addition, power demand is nearly price irresponsive (inelastic) in the short-run. Therefore, power prices often escalate to very high quotes (price spikes) when supply/demand conditions are tight. Most of these circumstances are short-lived, e.g. equipment outages, transmission congestion climatic events, etc., and price rapidly reverse to normal levels [6].
The exceptionally high volatility of electricity prices imposes high financial risks when trading electricity and forces generation companies to make decisions and commitments under high uncertainty. Thus, stochastic modeling and optimal decision making under uncertainty are key tasks in modern power trading and power risk management [7].
3.2. Trading power in spot markets
Currently, electricity is traded forward on bilateral negotiations and on centrally-run electronic platforms like power exchanges. Electricity can be traded in anticipation from one to three years (mostly OTC) to several months ahead. Shorter term forward markets negotiating electricity with delivery horizon of weeks, or even one day in advance in the so-called day-ahead markets are quite common. Market liquidity is typically higher as the contracting horizon shortens.
Because of technical requirements of real time system balance, spot power markets are always centralized and run by entities in charge of the physical operation of the system to keep reliability and security. Equilibrium spot prices are computed each 5 min, 30 min or in hourly basis according to the realized power demand and the last bid accepted for keeping the system balance in real time. Rigorously, locational marginal prices are set in order to account for transmission constraints. Therefore, spot prices reflect the actual conditions of the system at the time of delivery.
Though spot prices are subject to high uncertainty, liquidity of this market is warranted as the generator can always sell its production at real time prices. For this reason, the spot market is regarded a last-resort market. One advantage of participating only in this market is that unavailability of the generating unit does not have financial consequences for the generator other than the opportunity cost of the lost production.
Let pS(t) the prevailing price at the t-th time interval in the spot market, PS(t) the power delivered by the generating unit and Pmax its generating capacity. Under the hypothesis the generator is price-taker and its marginal costs of generation, denoted by MC(t), are constant with the rate of production, the optimal operating policy is:
\n\t\t\t\t
PS(t)={0 if pS(t)<MC(t)Pmax if pS(t)>MC(t)E42
\n\t\t\t\t
The operating profit BS(t) the generator obtains in the spot market by implementing this optimal production policy can be written as:
This equation shows the operating flexibility of generator to alter its output in response to the spot price in order to avoid operating losses if prevailing spot prices drop below marginal costs.
Figure 3 depicts the discontinuous nature of the profit function BS(t) when participating in the spot market, i.e. BS(t)≥0 for all prices. Indeed, this profit function can be assimilated to a call option with strike price MC. Figure 3 also schematically illustrates the probability density function (pdf) of the spot price of electricity f(pS). This function is typically highly right-skewed and presents strong leptokurtosis.
Figure 3.
Profit function of selling power in the spot market
The expected value of the profit in the spot market per unit of generating capacity bS(t) under the optimal operating policy is described by the following equation:
\n\t\t\t\t
E[bS]=∫0MC[0⋅pSf(pS)]dpS+∫MC∞[(pS−MC)f(pS)]dpSE44
\n\t\t\t\t
E[bS]=∫MC∞[(pS−MC)f(pS)]dpS≥E[pS]−MCE45
\n\t\t\t\t
It is noteworthy to observe that the first term in equation is the probability of obtaining a zero profit in the spot market. Note also that E[pS]=MC only if MC=0. By selling the production in the spot market, the generator never incurs in operating losses, i.e. Pr(bS<0)=0 as it can immediately stop production if pS<MC.
We consider now the more general case where generating units are unavailable, either for planned or unplanned reasons, during a fraction of the time. Let p the failure probability and q=1−p the probability of the unit being available, provided the failure and operating states are the only two mutually exclusive states in which the generator resides. We further assume that the price level and the state of the generator are statistically independent. Under these considerations, the generator cannot always capture de spread pS−MC and thus the probability of obtaining a positive profit will decrease accordingly. The expected operating profit under these conditions is then:
\n\t\t\t\t
E[bS]=q∫MC∞[(pS−MC)f(pS)]dpSE46
\n\t\t\t\t
The probability of having zero profit β0=Pr(bS=0) is given by:
\n\t\t\t\t
β0=p∫0∞f(pS)dpS+q∫0MCf(pS)dpS=p+q∫0MCf(pS)dpSE47
\n\t\t\t\t
The resulting probability density function of the hourly operating profit is illustrated in Figure 4. Despite the high variance of the profits, the function clearly shows that the generator cannot lose money when participating in the spot market, even if the unit is technically unavailable.
Figure 4.
Probability density function of the operating profit in the spot market of electricity
3.3. Trading electricity in forward markets
Given the dramatic volatility of real time electricity prices, a major activity of power trading is structuring hedging strategies by means of tradable derivative instruments like future and option contracts [8]. A power company owning a set of generating units may decide either to sell electricity in advance at a fixed price in a forward market, or wait to the time of delivery and receive the spot price. Deciding on committing production forward or being exposed to volatility of real-time power prices has however a drastic impact on risk.
By selling forward its production, the generator may hedge against a sudden decline of electricity spot prices during the delivery horizon, thereby securing an operating margin. This hedging strategy isolates the generator from the price risk. However, the generator in exchange resigns the opportunity of selling electricity in the spot market if high prices happen.
Electricity markets are typically arranged under a two-settlement system. This approach preserves the economy and efficiency of the physical operation of the power system from any financial commitment the market players have entered into in the past. Under the two-settlement scheme, only deviations from contractual obligations are negotiated in the spot market.
The revenue from the forward contracting is given by the volume sold PF times the price pF agreed in the forward contract, i.e. RF=pFPF. On the other hand, the revenue captured by selling in the spot market is given by RS=pSΔP=pS(PS−PF). So, the total revenue RF from forward contracting and delivering power in the spot market is given as:
\n\t\t\t\t
RT=RF+RS=pFPF+pS(PS−PF)=PF(pF−pS)+pSPSE48
\n\t\t\t\t
We can observe the utility of forward contracts by inspecting this equation. If generator delivers in the spot market an amount equal to its contractual obligation, i.e. PS=PF, the total revenue is set equal to pFPF irrespective of the fluctuations of the spot price pS.
At the time of delivery, and assuming the generator is price-taker, the term PF(pF−pS) is fixed and represents the profit of the forward contract against the spot market. Therefore, the profit-maximizing production policy is the same and given by the spot price, irrespective of the contractual obligations. The profit the generator can make by selling electricity in forward markets is given by the expression:
\n\t\t\t\t
BF=PF(pF−pS(t))+max[PS(pS(t)−MC),0]E49
\n\t\t\t\t
In Figure 5A, a probability density function of the spot price is depicted. In the following, it is assumed that the forward market price is an unbiased estimator of the spot price at the time of delivery. Therefore, the condition pF=E(pS) holds. In the forward contract, the generator makes a profit for unit of capacity bF=pF−MC, assuming pF>MC. Otherwise, the generator is better by avoiding entering into a forward obligation with negative profit. For realizing this profit, the generator must be able to deliver in the spot market the contracted volume in the exact amount. This profit level is achieved as long as the spot price exceeds the marginal cost, i.e. the probability of making this profit is Pr(bF=pF−MC)=Pr(pS>MC).
Graphically, this probability is represented by the dark grey area under the pdf of the spot price (cf. Figure 5A). The generator can make additional profits in the forward contract, bF=pF−pS>pF−MC, each time the spot price drops below the marginal cost, i.e. pS<MC. In fact, the generator is better buying replacement power in the spot market than incurring in fuel costs generating with its own facilities. Figure 5B illustrates the pdf of the profit of a forward contract. When compared with the profit distribution in the spot market (cf. Figure 5), it is easily noticeable the drastic reduction of the profit variance under forward contracting. The forward obligation sets a floor for profits, reducing dramatically dispersion of results and thereby the price risk. In exchange, the generator also foregoes the chance of profiting at times of high power prices in the spot market. The expected profit of a forward contract in terms of the pdf of the spot price f(pS) can be expressed as:
It can be mathematically demonstrated that under rational expectations and efficiency of forward markets, i.e. the forward price is an unbiased estimator of the spot price pF=E(pS), the condition E(bF)=E(bS) holds [1]. This means that for a risk-neutral generator both policies, either selling in the spot market or hedging in forward markets, are entirely equivalent. Nevertheless, for risk-averse players (which is the rule in real market settings), the hedged strategy is clearly preferred as profit expectation remain unaltered while price risk is eliminated.
3.4. Delivery risk in forward contracting
If we consider again unplanned outages of generating units, hedging price risk in the forward markets exposes generating companies to other class of risk, i.e. delivery risk, which also referred as quantity or volume risk. We further examine this important issue. When a generator under a contractual obligation is unable to deliver in the spot market the contracted amount, i.e. PS≠PF, the generator is forced to buy replacement power in the spot market at the prevailing price at that time. This may configure a very significant loss if while the generator is down the spot price is considerable higher than its own marginal costs, i.e. pS≫MC. Under this situation, the generator may be compelled to buy very expensive replacement power to honor the obligation, incurring in a potentially high financial loss. It is interesting to note that if when the unit is unavailable spot prices are lower or equal than the marginal cost, the generator can even make an extra profit bF=pF−pS>pF−MC. The probability density function of the forward position under consideration of positive failure probability and the associated delivery risk is illustrated in Figure 5C.
The expected profit can be computed as the expected value of the contract under the hypothesis of fully reliable unit times the probability of being available:
As for modern units q≅1, the change in the expected profit due to unit unavailability is typically negligible. However, downside risk increases substantially.
Assuming statistical independence between the unit´s failure and the level of spot prices, the probability of incurring in losses is given by:
\n\t\t\t\t
Pr(bF<0)=pPr(pS>pF)E52
\n\t\t\t\t
and the conditional expectation on the value of losses can be written as:
\n\t\t\t\t
L=∫pF∞(pF−pS)f(pS)dpS|bF<0E53
Figure 5.
Probability density function of operating profits for a forward contract
From the profit pdf of the forward contract f(bF), the downside risk metrics, namely the value at risk (VaR) and the conditional value at risk (CVaR) for a δ confidence level can respectively be computed as:
\n\t\t\t\t
Pr(bF<−VaRδ)=∫−∞−VaRf(bF)dbF=δE54
\n\t\t\t\t
CVaRδ=∫−∞−VaRbFf(bF)dbFE55
4. Problem modeling
4.1. Problem formulation
Let consider a small generation portfolio running in an electricity market. The power company owning the generation portfolio wants to determine the best-selling strategy of the energy production, which would maximize the expected profit while financial risk is constrained. Any trading strategy x is defined by the amount of energy to be sold in each different available selling instrument i in the electricity market, for example, the spot market, day-ahead obligations, annual forward contracts, etc.
It is important to notice that the trading strategy sets the amounts of energy committed in every forward instrument, but only estimates the amount of energy to be actually sold in the spot market. Indeed, actual energy production is stochastic and depends on technical availability of generating units and the spread between spot and fuel prices. Suppose that whatever trading strategy is decided now, it could be changed in future decision stages in order to rebalance the portfolio. Composition of the portfolio can be rebalanced only at a cost however, i.e. the transaction costs. The process is then a sequence of balancing decisions determined by the trading strategy, each followed by a stochastic reward according to the position taken in the market. The process is over after a number of periods n.
The optimization of the trading strategy can mathematically be formulated as a stochastic non-linear problem involving the maximization of the expected profit accrued by the generation portfolio across all instruments i and time intervals t:
\n\t\t\t\t\tpFi,tw: Effective future price of instrument i in period t and Monte Carlo sample w\n\t\t\t\t
\n\t\t\t\t\tpShw: Spot price in hour h and Monte Carlo sample w\n\t\t\t\t
\n\t\t\t\t\tCtw: Costs of energy in period t and Monte Carlo sample w\n\t\t\t\t
\n\t\t\t\t
Ctw=MC⋅∑h∈tEhwE62
\n\t\t\t\t
\n\t\t\t\t\tMC: Constant marginal cost of generation.
\n\t\t\t\t\tTw(xi,t,xi,t−1): Transaction costs due to the change in the amount of instrument i held in the portfolio after the rebalancing decision at the beginning of period t\n\t\t\t\t
\n\t\t\t\t\tpFiw,h0t: Forward price of instrument i and Monte Carlo sample w at the beginning of period t.
The constraint (57) represents the hourly energy balance for all Monte Carlo samples and forces the generator to settle in the spot market differences between the energy sold in forward markets and actual production. Constraints (58) and (59) are introduced to avoid financial positions without physical counterpart, i.e. avoid the generator to take speculative positions by selling in forward markets more energy that the generation portfolio can produce. These constraints may be replaced by capital restrictions as regulations often allow financial trading without physical position. Finally, constraint (60) represents the limit to the financial risk associated to the selling strategy within each period t. In order to limit risk over the horizon time, the selected risk metric must be coherent [9] ensuring subadditivity. There are several downside risk measures that fulfill this requirement among which the Conditional Value at Risk (CVaR) is the most widely used (cf. equation 55).
The equation (61) represents the revenue generated by each forward contract and the revenue (or cost) due to selling (or buying) differences between energy sold in futures and the real generation in the spot market forced by restriction (57). The equation (62) represents the costs of the operating policy, which is independent of the forward obligation for a price taker as demonstrated in Section 3. Therefore, the three equations (57), (61) and (62) calculated for the whole Monte Carlo set represent then the profit calculated by equation (49) of Section 3. Finally the transaction costs in equation (63) are assumed to be the 3% of the total transacted value only for forward contracts, and not existent for the spot market.
4.2. Modeling stochastic spot and forward electricity prices
The problem formulation relies on Monte Carlo simulations to represent uncertainty on the future development of key variables. In addition, stochastic simulations are used to confront the algorithm with mapping scenarios for approximating both, the Value and the Risk functions.
A synthetic ensemble of 2000 annual realizations of hourly power prices in the spot market were generated by means of spectral representation techniques [10]. Forward prices and spot prices are not statistically independent. The forward prices corresponding to each spot price sample are calculated considering both, the expected value of the spot price and the mean value of each spot price time series. To simulate the changes in the forward prices accounting for the correlation to each sample of the spot prices a simple model is introduced.
Under perfect competition and rationality on the expectation formation, the price of a forward product should converge to the mean expected spot prices for the delivery period. This is relatively easy to calculate for the first hour of the simulated time. Assuming that the whole Monte Carlo set of spot prices was simulated taking the same price forecast as the market, the price of any forward should be the mean spot prices for the delivery period:
\n\t\t\t\t\tHi: delivery period for instrument i\n\t\t\t\t
\n\t\t\t\t\tH0i: first hour of delivery period for instrument i\n\t\t\t\t
For the first hour, this model represents the expectation of the market for each delivery period. However, this expectation should change according to the values that the spot prices take in each sample and the information gathered by a virtual market taking place in each particular sample path. If perfect foresight is assumed, each virtual market could calculate without uncertainty the hourly forward price for its particular spot prices sample path simply as:
\n\t\t\t\t
pFBiw,h=1Hi−(hid−1)∑j=hidHipSw,jE66
\n\t\t\t\t
The equation (64) represents a model where any additional information that arrives as the spot price of the particular sample path is different form the forecasted in the first hour is dismissed by the virtual market. Thus, this model is representative of reality only for the first hour of simulation where no additional information could have been gathered by the virtual markets. On the other hand, the equation (66) represents a model where all the additional information is obtained beforehand for each Monte Carlo sample. Likewise, this model is suitable only for the last hour of simulation where all information is already known by the virtual market within each Monte Carlo sample. Finally, the two models can be combined, in order to simulate the forward price dynamics in correlation to the spot prices of each Monte Carlo sample path. In this work, it is assumed that the information gathered by each virtual market grows linearly, augmenting for each hour
The assumption that the information is linear with time could be replaced with a more complex information model, such as a function of the cumulated difference between the initial forecasted spot price and the particular spot prices of each Monte Carlo sample path.
Finally, a contango situation is considered in the forward market. A risk premium of 8% in excess of expected spot prices is considered for the forward prices in the future market to compensate for the volatility risk. This premium reduces linearly during the delivery period of the future and becomes zero at the last hour of delivery.
The model for future prices for each instrument i, hour h and sample w is thus as follows:
where β is the risk premium paid in excess to the expected spot price and set β=8%.
4.3. Reliability model of the generation units
Other relevant source of uncertainty considered is the random failure of the generating units. The stochastic model of generator outages is built considering that the unit can reside in four mutually exclusive states: Operation (required), Reserve (not required), Unavailable (required) and Unavailable (not required), as shown in the diagram of space states in Figure 6 [11].
Figure 6.
Four-state stochastic model of the generation units
This unit model accounts for the fact that peaking units exhibit higher availability rates. This result is explained by the fact the failure probability is typically very small when the unit is in the stand-by state. A generator is economically called online if its marginal cost of production is below the prevailing spot prices, following the decision model of equation (42). Variable costs of generation are assumed linear with power output, i.e. marginal costs are constant.
The operation-failure cycles of the generating unit are obtained from a chronological Markovian stochastic simulation. For each spot price sample, a time series of power output is synthesized for every generation unit. The hourly power output is simulated o following three steps:
Based on failure and repair rates defined by the state the unit resided in the previous hour, a random failure is simulated [12]. If a failure is in place, the output power is set to zero for this particular hour.
The dispatch of the unit is simulated, taking into account the marginal cost of generation and the prevailing sample spot price at that time interval. Here perfect foresight of the spot price is assumed in order to decide the dispatch and fulfill the minimal generation times.
If dispatched, other unit’s technical restrictions are fulfilled, e.g. ramping capabilities.
This chronological stochastic model reproduces with accuracy the dynamics involved in failure and repair cycles of generators, giving the possibility to select different failure rates depending on whether the unit is generating or is in stand-by.
4.4. Risk constraint formulation
As already mentioned before, the financial decision process can be modeled by means of a MDP. Naming profit Bw for each sample the sum of income, cost and transaction cost over all instruments, the objective function (56) becomes:
\n\t\t\t\t
max x[E[∑t[Bw(xt,xt−1)]]]E70
\n\t\t\t\t
Exchanging the order of the expectation operator and the summation, and expanding the summation:
\n\t\t\t\t
max x[E[B1w(x1,x0)]+∑t=2nE[Btw(xt,xt−1)]]E71
\n\t\t\t\t
where n is the total number of time periods considered.
At this point and considering the first term depends only on the initial market position x0 and the first strategy decision x1, the maximization can be decomposed using the Bellman´s Principle of optimality as follows:
\n\t\t\t\t
max x1[E[B1w(x1,x0)]+maxxt=2…n∑t=2nE[Btw(xt,xt−1)]]E72
\n\t\t\t\t
If x is independent of the Monte Carlo sample, the terms inside the summation over all future periods (t=2…n) are simply the expected profit for the period t after a decision xt−1→xt. However, the model should take into account that future strategy decisions may be different for each Monte Carlo sample, accounting for adjustments the decision-maker almost certainly would execute to face specific scenarios. Then, the expected profit Bt¯ and the decision itself for future decision stages will depend on a set of variables st, which represent the variables considered by the decision maker in order to adapt the strategy to a particular situation and the equation (72) becomes:
\n\t\t\t\t
max x1[B1¯(x1,x0,s1)+maxxt=2…n∑t=2nBt¯(xt,xt−1,st)]E73
The maximization can be solved by a set of recursive maximizations, each one solving only one decision stage:
\n\t\t\t\t
max xt[Vt(xt,xt−1,st)]E75
\n\t\t\t\t
With this model, the optimization can be decomposed in steps and the dynamic nature of a strategy can be accurately replicated. Despite the fact future trading decisions xt=2…n are considered and optimized, the practical product of this procedure is the new optimal rebalanced state xt=1 starting from the previous trading position xt=0. The further trading positions are only optimal given the current information available and should be reconsidered later. Therefore, each new trading position (xt=2, xt=3,…, xt=n) should be the product of a similar optimization incorporating the additional market information available immediately before.
The value functions provide the expected continuation value within a state space defined by the state variables, xt,xt−1 and st
There are several other decomposition methods, some of which exclude the decision as state space variable defining the value functions in a post-decision state space. These approaches make the step maximization sometimes harder but present advantages such as a state space of fewer dimensions. See for example [2].
. However, the continuation functions, which are essential to solve the optimization problem, are unknown beforehand. It is here that the ADP approach is introduced to approximate the value and risk functions for the state space.
The set of constraints remains the same as they were already defined for each period t. Nevertheless, special considerations should be made to calculate de risk and to fulfill the risk-constrained optimization. As Vt only account for the expected profit, risk functions Rt should also similarly be calculated for the same state space in order to enforce the risk constraint.
By definition, the linear regression will deliver an approximation that minimizes the mean square error on the entire dataset. In a stochastic setting where the same inputs leads to several different outputs, the regression will accurately estimate the expected value for a given set of inputs, provided the sample size and the approximation order are appropriate. This fits perfect for the case of the value function but for the approximation of the risk function some problems arise.
Let suppose that the CVaR is chosen as risk metric. As the algorithm progresses, new data points are collected, i.e. a set of state variables and its corresponding simulated profits for the period. For approximating the CVaR associated to a particular point in the state space, one approach is to select a subset from the dataset whose input variables are “close” to the point. Then the CVaR is calculated first by sorting the profit values and then taking the mean of those below the specified α-quantile. Now, let suppose that a new data point is simulated and an update of the CVaR approximation is needed. To do so, the process described must be repeated, but now including the new data point. This simple approach has large disadvantages: all the data points must be stored and the mean is not easily updated as old data may be excluded or included of the zone below the α-quantile. These drawbacks are caused by the fact that the CVaR is quantile-based. To solve these difficulties, another solution is envisioned. Instead of approximating directly the CVaR, another risk measure is used to approximate the CVaR within the space state. The risk measure used is called Relative Lower Semideviation (RLS) and it is moment based instead. Hence, it can be updated more easily and without needing to store the entire dataset. These types of risk measures are described in detail in [13-16] and for a stochastic profit Pt have the form:
\n\t\t\t\t
RLS(Bt)=−E[Bt]+a⋅σp−[Bt]E76
\n\t\t\t\t
σp−[Bt]=(E[max(E[Bt]−Bt,0)p])1pE77
\n\t\t\t\t
where the equation (77) is the negative semideviation of degree p of the stochastic profit Pt.
It can be proven that these moment-based risk measures are coherent if 0≤a≤1 and p≥1. To approximate a CVaR with a 5%-quantile, in this work the parameters used are a=1 and p=9.5. To compute the approximation, two linear regressions were used to calculate the expectations on the profit and on the negative deviations, which can be updated using the same method proposed for the value functions. In a Monte Carlo scheme, a large amount of data is needed to compute reliable risk estimations. Therefore, the risk constraint should not be enforced at the early iterations of the ADP algorithm, simply because the sample size of the dataset is small to get a consistent and statistically converged risk value.
5. Numerical case study
5.1. Algorithm validation
With the objective of validating the results of the proposed ADP algorithm, a first simple exemplary case is considered in which a thermal generator sells energy in the spot market and in a future contract. The results of the ADP algorithm were compared with the results of a conventional DP algorithm for which the space-state was discretized appropriately.
The fractions of energy sold in the spot market and in a quarter future contract were optimized considering that the future can be traded during the delivery period. The optimization determines three decision stages during this period, one at the beginning of each month, consisting on sell or buy energy in the future market based on the previous state. The space-state previous to each decision is defined only by the level of future already sold, in order to keep tractable the DP problem.
5.2. Validation results
The results obtained by solving the problem by means of the ADP and DP algorithms are presented in Figure 7. The plots represent the expected profit and the downside risk measured by the CVaR of the optimal strategy as a function of the initial state, i.e. the energy already committed in the future contract at the initial stage. An excellent agreement between the optimal strategies obtained by ADP and DP is evidenced, validating the proposed approach.
It can be noticed that the expected profit rises as the amount of energy sold forward increases. This is caused by the risk premium paid to the generator in the future market, i.e. the mean future prices are higher than the mean spot prices. Additionally, the transaction costs are not compensated by the risk premium; therefore the best trading strategy is to maintain similar involvement in the future market to the initial level without rebalancing the portfolio. This is illustrated in Figure 8, where the optimal decisions for the first month are practically the same when solving with a conventional DP and an ADP approach.
Figure 7.
Expected benefit and downside risk of the optimal strategy – Validation case.
It is noteworthy to observe in Figure 7 that financial risk lessens when forward contracting in the future market increases. This means that for the conventional generator considered, which present a high availability, the delivery risk is lower than the risk of not being dispatched in the spot market. The behavior of the risk curve is closely related to the unit’s failure and reparation rates and to the marginal production costs. Generators with low marginal costs are in the first places of the dispatch merit order, and hence the risk of not being dispatched is low. Moreover, high failure rates imply also a higher delivery risk. Out of these relations arise a broad number of risk curves that differ from one generator to another and suggest that considerable risk mitigation by aggregating different generators in a portfolio is possible.
Figure 8.
Optimal percentage of energy sold in the future market for the first time period – Validation case
5.3. Optimal policy for portfolio rebalancing
For the base study case, a slightly more complex system was examined. In this case, the generation portfolio comprises five generation units of 2 MW each with a constant marginal generation cost of 50$/MWh, failure rate of 1/950h-1, reserve failure rate of 1/9950 h-1 and repair rate of 1/50 h-1. These rates give a failure probability of 5% while the unit is in operation and of 0.5% while in standby.
An annual future, four quarterly futures and the spot market were considered together with 12 monthly trading decision stages for rebalancing the portfolio. The space state for this arrangement is defined by the amount sold forward as well as spot and future prices before each decision point and for each realization. Therefore, the decisions are chosen taking into account additional information about the state of the market for each realization. Even though the simplicity of the example, the additional variables cause a drastic increase in the dimension of the problem and would force an unacceptable coarse discretization of the space-state in order to keep the problem tractable with conventional DP. In this case, the maximum admissible expected loss for the 5%-confidence level is set to CVaR5%=$20000 for each decision stage. Since the risk measure is coherent, i.e. sub-additive, the annual risk is less or equal to $20000∙12 periods=$240000. Transaction costs are set to 3% of the dollar amount contracted in the forward market. The optimization problem was solved on a Beowulf cluster comprising 20 multicore Intel i7 2600K 3.4 GHz processors connected by a Gbit LAN. The 160 available computation nodes were fully exploited and the total computation time was 5 h.
In Figure 9, the results of the ADP algorithm for the optimal strategy on the first rebalancing period with a previous trade equal to zero are illustrated. The amount of energy to be committed in each market is expressed in terms of a fraction of the maximal energy output the generation portfolio would generate without failures in the period, i.e. 10MWh per hour of operation. The prices for the traded futures are also presented in Figure 9 except for the 2nd quarter future which is 45.63$/MWh and it is not shown as the optimal trade does not include this contract, presumably because the price is too low and it is better to wait for a better price in the spot market and sell in subsequent decisions. Likewise, the expected spot prices for each quarter are displayed, except for the last quarter which is 52.45$/MWh. Finally, the expected annual profits without considering fixed costs and the risk estimated by the RLS for the first rebalancing period are shown. Note that the expected profit is calculated considering that the following trading decisions are made taking into account the particular sample price realization, capturing the adaptation to the market developments. Thus, the rebalancing decisions for the second up to the last period are not unique.
5.3.1. Sensitivity to unit availability
In order to investigate the sensitivity of the optimal trading strategy to the unit availability, a second case was considered. Under these conditions, all the parameters are identical to the described base case, except for the failure and repair rates. Operation failure rate was set to 1/850h-1, a reserve failure rate to 1/9850 h-1 and repair rate of 1/150h-1. With these rates, the failure probability is 15% in operation and 1.5% in standby.
Figure 9.
Optimal trading strategy for a 5x2MW generation portfolio
In Figure 10, the optimal trading strategy delivered by the ADP algorithm for the case of decreased unit availability is depicted. The differences on the sell strategy are evident for the annual future and the total amount of energy left to be sold in the spot market. Because of units have more frequent and longer random failures, a long-term commitment is avoided. However, the high prices for the fourth quarter push the sell in future markets up to 100%. In comparison to the base case results, the expected annual profit is slightly lower. The risk for the first month is irrelevant, because the forward commitment is around 50% and the probability of having more than two units (> 40%) unavailable is very low, leading to almost all unit failures can be covered by the remaining available units.
Figure 10.
Optimal trading strategy for a 5x2MW generation portfolio with reduced availability
5.3.2. Sensitivity to transaction costs
The third case of study is identical to the base case but considering a transaction cost of 7% of the sold amount instead of 3%. The optimal trading policy for the case of increased transaction costs are shown in Figure 11. In this case, the generator sells only 1.32% of its capacity in an annual future contract due to the irreversibility introduced by the higher transactions costs and the larger contracting volume. Under these circumstances, the risk premium offered in the annual future price render insufficient for attracting the generator to enter in such a long-lasting commitment. On the other hand, the sell volume in quarterly futures is higher than in the base case, staying between 62% and 76% in a rather static trading policy. Under higher transaction fees, it is desirable to be able to rebalance the portfolio in future stages with smaller changes and hence smaller transaction costs. Expectedly, the expected profit is lower due to higher costs. The delivery risk for the first month is negligible, due to the fact that the most likely failures can still be covered by the remaining operative units without buying replacement power in the spot market.
Figure 11.
Optimal trading strategy for a 5x2MW generation portfolio with higher transaction costs
6. Conclusions
Optimal decision-making under uncertainty is a field of active research and uppermost relevance in science, engineering and computational finance. Conventional optimization approaches have difficulties and serious limitations for tackling high-dimensional problems often encountered in real world settings. Recent advances in operation research and computation technology opened new possibilities for approaching optimization problems that were considered intractable until recent times. This chapter presents an efficient Approximate Dynamic Programming algorithm for solving complex stochastic optimization problems and amenable for running in a distributed computing environment. The implemented ADP algorithm has been validated against conventional Dynamic Programming for a simple problem.
The proposed algorithm uses Monte Carlo simulation techniques combined with linear regression for successively approximating and refining the continuation and risk functions. A novel and efficient procedure for updating these functions, combining calculations of independent computing threads and without storing the entire datasets, is proposed. This feature enables exploiting the currently widespread multicore processor architectures and deploying the algorithm in large computation clusters.
In order to demonstrate the practicability of the envisioned approach, the proposed algorithm has been applied to find the optimal trading strategy of a power generation portfolio in forward and spot electricity markets. Power trading and risk management is currently a central activity of power companies running in liberalized electricity markets. The probability density functions of the profits a generator would make by participating in either the spot or the forward markets are extremely different. The forms and boundaries of these probability functions have drastic implications for risk when generators get involved in the spot or the forward markets. Generators can hedge price risk of spot markets by contracting forward, but by exposing themselves to delivery risk. Hence, the optimization problem is formulated as the maximization of the expected profit of the trading policy while the downside risk is constrained. For doing so, the generator selects and combines a portfolio of annual and quarterly forward contracts as well as involvement in the spot market. A frictional market with non-negligible transaction costs is considered.
A detailed chronological 4-state reliability model of generating units has been adopted for replicating stochastic behavior of random outages. Large stochastic ensembles of spot prices and forward prices time series have been synthetized for this application. In order to retain subadditivity, downside risk is measured by CVaR. The approximation of CVaR by a moment-based risk metric drastically improves computational efficiency while providing accurate and consistent risk estimations.
Applying ADP-based optimization techniques to electricity markets is a novel undertaking and opens a prospectively fertile avenue for research. In future works, further algorithmic enhancement are foreseen. Application of these methods for designing trading strategies that considers a larger set of available financial contracts as well as generation portfolios comprising renewable resources would provide results and findings of high practical significance.
Acknowledgments
This work was supported in part by the National Scientific and Technical Research Council (CONICET) and the Agency for Promotion of Science & Technology (ANPCyT), Argentina. The financial support of the German Academic Exchange Service (DAAD) is also gratefully acknowledged. The authors also thank the support of the colleagues at the Institute of Power Systems and Power Economics (IAEW), RWTH Aachen, Germany, especially Univ.-Prof. Dr.-Ing. Albert Moser as well as colleagues at Institute of Electrical Energy (IEE), National University of San Juan, Argentina.
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Introduction",level:"1"},{id:"sec_2",title:"2. Approximate dynamic programming",level:"1"},{id:"sec_2_2",title:"2.1. Regression technique",level:"2"},{id:"sec_3_2",title:"2.2. Kernel functions",level:"2"},{id:"sec_4_2",title:"2.3. ADP and linear regressions in the context of distributed computing",level:"2"},{id:"sec_6",title:"3. Electricity trading",level:"1"},{id:"sec_6_2",title:"3.1. Electricity markets",level:"2"},{id:"sec_7_2",title:"3.2. Trading power in spot markets",level:"2"},{id:"sec_8_2",title:"3.3. Trading electricity in forward markets",level:"2"},{id:"sec_9_2",title:"3.4. Delivery risk in forward contracting",level:"2"},{id:"sec_11",title:"4. Problem modeling",level:"1"},{id:"sec_11_2",title:"4.1. Problem formulation",level:"2"},{id:"sec_12_2",title:"4.2. Modeling stochastic spot and forward electricity prices",level:"2"},{id:"sec_13_2",title:"4.3. Reliability model of the generation units",level:"2"},{id:"sec_14_2",title:"4.4. Risk constraint formulation",level:"2"},{id:"sec_16",title:"5. Numerical case study",level:"1"},{id:"sec_16_2",title:"5.1. Algorithm validation",level:"2"},{id:"sec_17_2",title:"5.2. Validation results",level:"2"},{id:"sec_18_2",title:"5.3. Optimal policy for portfolio rebalancing",level:"2"},{id:"sec_18_3",title:"5.3.1. Sensitivity to unit availability",level:"3"},{id:"sec_19_3",title:"5.3.2. Sensitivity to transaction costs",level:"3"},{id:"sec_22",title:"6. Conclusions",level:"1"},{id:"sec_23",title:"Acknowledgments",level:"1"}],chapterReferences:[{id:"B1",body:'Olsina F, Larisson C, Garcés F. Hedging and volumen risk in forward contracting [in Spanish]. Proceedings of XII ERIAC de CIGRE, Foz do Iguazu, Brazil, May 2007. paper C5.02.'},{id:"B2",body:'Powell WB. Approximate Dynamic Programming: Solving the curses of dimensionality. Wiley-Blackwell; 2007.'},{id:"B3",body:'Bertsekas DP,Tsitsiklis JN. Neuro-Dynamic Programming (Optimization and Neural Computation Series, 3). Athena Scientific; 1996.'},{id:"B4",body:'Bellman RE. Dynamic Programming. Princeton University Press, Princeton, NJ; 1957 '},{id:"B5",body:'Stoft S. Power System Economics: Designing Markets for Electricity, New York, USA; IEEE Press/Wiley; 2002.'},{id:"B6",body:'Hadsell L, Maralhe A, Shawky, HA. Estimating the volatility of wholesale electricity spot prices in the US. The Energy Journal 2004; 25(4) 23-40.'},{id:"B7",body:'Bjorgan R, Liu C, Lawarrée J. Financial risk management in a competitive electricity market. IEEE Transactions on Power Systems 1999; 14(4) 1285-1291.'},{id:"B8",body:'Stoft S, Belden T, Goldman C, Pickle S. Primer on Electricity Futures and Other Derivatives. Lawrence Berkeley National Laboratory, University of California, LBNL-41098, 1998.'},{id:"B9",body:'Artzner P, Delbaen F, Eber JM, Heath D. Coherent measures of risk. Mathematical Finance 1999; 9(3) 203-228.'},{id:"B10",body:'Olsina F, Weber C. Stochastic simulation of spot power prices by spectral representation. IEEE Transactions on Power Systems 2009; 24(4) 1710-1719.'},{id:"B11",body:'Billinton R, Ge J. A comparison of four-state generating unit reliability models for peaking units. IEEE Transactions on Power Systems 2004; 19(2) 763–768.'},{id:"B12",body:'Billinton R, Allan R. Reliability evaluation of power systems. Plenum Press, New York, 1996.'},{id:"B13",body:'RockafellarR, Uryasev S, Zabarankin M. Generalized deviations in risk analysis. Finance and Stochastics 2006; 10(1) 51-74.'},{id:"B14",body:'Fischer T. Risk capital allocation by coherent risk measures based on one-sided moments. Insurance: Mathematics and Economics 2003; 32(1) 135-146.'},{id:"B15",body:'Pavlo AK. Higher moment coherent risk measures. Taylor & Francis, 2007.'},{id:"B16",body:'Rockafellar T, Uryasev S, ZabarankinM. Deviation measures in risk analysis and optimization. University of Florida, Department of Industrial & Systems Engineering Working Paper 2002.'}],footnotes:[{id:"fn1",explanation:"The assumption that the information is linear with time could be replaced with a more complex information model, such as a function of the cumulated difference between the initial forecasted spot price and the particular spot prices of each Monte Carlo sample path."},{id:"fn2",explanation:"The prices for the first hour in a real market situation should consider the real market prices."},{id:"fn3",explanation:"There are several other decomposition methods, some of which exclude the decision as state space variable defining the value functions in a post-decision state space. These approaches make the step maximization sometimes harder but present advantages such as a state space of fewer dimensions. See for example [2]."}],contributors:[{corresp:"yes",contributorFullName:"Miguel Gil-Pugliese",address:"miguel.gil.pugliese@gmail.com",affiliation:'
Institute of Power Systems and Power Economics (IAEW), RWTH Aachen, Germany
Institute of Electrical Energy (IEE), National University of San Juan, Argentina
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1. Introduction
Mortars are among the first building materials used in constructions, even from prehistoric times. Their study reveals a great source of information regarding the evolution of their technological characteristics and application techniques, the availability and exploitation of raw materials, as well as the wider socioeconomic aspects of each era. In any case, it seems that ancient masons were fully aware of the significant role of mortars in constructions and could exploit the raw materials that were available along with the application techniques [1]. In particular, the role of the quality of aggregates on the properties of old mortars has been known since, at least, Roman times. Natural sands of different origins and nature (river, quarry, sea) and crushed bricks combined with binders which were usually lime-based, were used for many centuries (Figure 1). These mortars were of different types and served as bedding, renders or plasters, floors, and mosaics’ substates forming masterpieces of the world cultural heritage [2].
Figure 1.
Coarse aggregates of natural origin in bedding mortars of the fourth century AD (left) and crushed brick as aggregates in a bedding mortar of the sixth century AD (right).
It is evident from the classic authors that the Romans preferred sharp sands to rounded sands, as they knew that these would produce stronger mortars; for example, Palladius, Pliny, and Vitruvius refer to recipes and guidelines for criteria that can be used for sand selection in the mixtures [3, 4, 5]. Among the requirements they mention, the origin, the shape, and the cleanness of the sand are the ones that prevail. They noticed the direct relation of the sand quality to the setting and strength of the mortars, and they gave precise directions to avoid, for example, sea sand due to salt contamination that can accelerate the weathering of the mortar. Manufacturing mortar was the first milestone in building history which has been continuously evolved up to the modern concrete. The materials used for mortar manufacture since antiquity were binders (clay, lime, pozzolan, brick/tile dust), aggregates (sand, gravel, crushed brick, pumice), and materials that were less frequently found and used (such as chopped straw, egg whites, reeds, blood, palm fibers, milk, and goat hair.). In Akrotiri of Thera, Greece (1700–1400 BC), structural mortars were made of local origin clay, mixed with gravel, charcoal, and straw [6]. In Hellenistic monuments, such as Dilos residences (second century BC), lime-pozzolan mortars were mainly found, with aggregates of natural origin and of granulometry mainly 0–8 mm [7]. During the Roman period (second century BC–third century AD), the use of lime and pozzolan dominated in constructions, while brick dust and crushed brick also started to be used [8]. The systematic and in high proportion use of brick dust and crushed brick in lime or lime-pozzolan mortars were expanded during the Byzantine era fourth–fifteenth century AD [9]. Aggregates (natural origin and crushed brick) were of gradation 0–8 mm to 0–16 mm, with a B/A ratio 1/2–1/3 [10]. The effectiveness of the adhesion between the binder and the crushed brick aggregates achieved in those cases was impressive. During the Ottoman period (fifteenth–nineteenth century AD), structural mortars were manufactured by using the available raw materials [11]. They were mainly lime-based (pure lime or lime with clay), while in specific constructions demanded in resistance to humidity (baths, cisterns), pozzolan and brick dust were also added. Aggregates were of natural origin (in some cases crushed brick was also added), of 0–8 mm granulometry, and of B/A ratio 1/2 [12]. In Medieval times (fifteenth–nineteenth century AD), structural mortars mainly consisted of lime (in some cases pozzolan was added), natural or crushed aggregates, and crushed brick in gradations 0–4 mm to 0–8 mm and B/A ratio 1/1–1/2 [13]. During the nineteenth and beginning of the twentieth century, structural mortars varied depending on the building type and the local constructional tradition. Aggregates were usually of the natural origin of 0–8 mm gradation [14]. Later, scholars such as Lanas [15] referred to the importance of the binder/aggregate interface as a zone that requires special attention. From the historic research of the components of mortars, it is obvious that the presence of sand was catalytic and continuous. In relation to the origin of the sand used, it was mainly local, from streams or rivers, and in special cases, crushed bricks or tiles in different gradation were added [16, 17]. Aggregates are the most ubiquitous materials in construction. Nowadays, the building industry uses aggregates as materials for construction, mainly in their bound state with cement to form concrete, bitumen to form asphalt, or as components for composite materials. Nevertheless, the utilization of aggregates has a long history in construction technology and especially in mortars. Over the last decades, due to the increasing cost of raw materials and the continuous reduction of natural resources, the recycling of industrial waste has become an interesting option for the building industry. Nowadays, many large industries use manufactured sand alone for producing mortar by partially replacing river sand. In these complex systems, the aim seems to be first, the utilization of low-cost materials from local resources and second ensuring the quality and performance of materials for specific applications. Therefore, there is still a continuous usage of sand in construction works. Alternative approaches to completely replacing sand in mortars have intensified over the last decades [18]. At the moment, the increasing awareness of society about safeguarding heritage buildings and at the same time protecting the environment promotes strategies of combining principles of restoration with environmental friendly materials and techniques.
2. The influence of sands in mortar’s properties
2.1 Mortars as composite materials
Composites are materials made by combining two or more other materials. These materials are important in the construction sector as building technology has been favored by the advanced properties that composites can offer. The development of composite materials along with related design and manufacturing technologies constitute one of the most important advances in the history of materials. Composites are multifunctional materials having unprecedented mechanical and physical properties that can be tailored to meet the requirements of a particular application [19]. Thus, new achievements have been constructed as the innovative composites could add new possibilities to the engineers’ imagination.
Mortars are a specific type of composite material, which consists mainly of three phases—paste as the matrix, interface transition zone (ITZ), and aggregates. The properties of mortars are influenced by:
Aggregates (type, percentage, shape)
Binders (activity, percentage)
Their contact surface area
Mixing water
The maintenance conditions of the applied specimens
At fresh state, mortar should be workable (do not break and do not flow), plastic (to have consistency to hold and not flow on overload loads) and it should show volume stability (do not cause contractions or expansions). At harden state, it should have the required strength and the required porosity. Aggregates constitute the strongest phase, hold a significant percentage in the volume of mortar and are frequently used in sand size (up to 4 mm). Conditions for the use of aggregates in mortars is the health of both the parent rock and the grains (without breaks, cracks, impurities), low porosity—small absorption index, homogeneous granulometric grade, percentage of the fines (<0.075 mm) should not exceed 5% [20]. The presence of fines in lime-based mortars can cause considerable alterations to the properties of the mortars. Their presence significantly reduces the strength and increases the volume shrinkage of mortars [21]. Furthermore, the porosity can be increased, and the same can also happen with capillarity when fine aggregates are participating in excess. Additionally, the type of fines also seems to play a role in their behavior in relation to the basic binder. For example, the strength is decreased in compositions with clay fines while porosity is affected mainly by limestone fines [22]. Capillarity also seems to be affected by the type of fines as the compositions containing fines 10–15% presented low absorption probably because fines block capillary pores [23].
In the case of mortars, as composite materials and keeping in mind that aggregates retain the inherent properties of the rocks from which they are derived, it can explain that the color, the chemical and physical characteristics of the aggregates directly affect the specific weight, the measure of elasticity, the volume stability, the appearance, and the mechanical and physical properties of mortars. The addition of sand to a binding system in mortars technology has proved to confer technical advantages as they contribute to volume stability, durability, and structural performance [16]. The gradation of the aggregates was wide, but the most adequate part was sand of 0–4 mm. Coarse aggregates up to 1 cm were used in thick joints [10] and also combined with sand for structural mortars while sand or finer aggregates (0–2 mm) are usually the constituents of the renders or plasters [24]. Usually, aggregates are obtained after the gentle grinding and sieving (based on EN1015-1) [25]. Even homogeneous distribution of grains is usually obtained as shown in Figure 2 in a typical bedding mortar.
Figure 2.
Typical gradation of old structural mortar.
The ratio of binder to aggregates (B/A) ranges widely but it could be said that for most of the structural mortars, it is 1:2.5 or 1:3 while for the renders and plasters are richer in binder content and the ratio is mostly 1:1 or 1:1.5 [23]. Apart from the different types of aggregates, as their mineralogy and origin are concerned, the volume content in the mixture, the maximum size, and their gradation influences the structure of a binder—aggregate mixture and the performance of mortars overall [10]. The added aggregates strengthen the composite, and the associated interface weakens it. These two opposite effects offset each other, and the combination of them leads to declined strength. Generally, a strong cohesion between the mortar binder and coarse aggregate confirms the good masonry properties. On the other hand, the increase of aggregate content reduces the workability of a mix and thus, reduces the strength as well [26]. It has been mentioned before that aggregate plays a role in restraining the shrinkage of cement paste, and that the shrinkage of the aggregate itself can be neglected [27]. It has been found in various composite materials that a certain amount and proper size of the aggregate are beneficial to the strength and fracture energy of the composite [28]. For mortar specimens, aggregates have a significant influence on both rheological and mechanical properties. Their specific gravity, particle size distribution, shape, and surface texture influence markedly the properties of mortars in the fresh state. On the other hand, the mineralogical composition, toughness, elastic modulus, and degree of alteration of aggregates are generally found to affect the properties of mortars and in the hardened state [29]. The drying shrinkage strains in investigated mortars are changed significantly by different kinds of fine aggregate materials. The water content of the mortar mix proportion is a major factor in drying shrinkage evolution. Increasing the unit water content can result in an increase in the amount of capillary water, and hence more shrinkage strain would be obtained. The bonding stress of the weak interface zone between the coarse aggregate and paste can be improved when a chemical reaction between the aggregates and the paste [30].
More recently, the role of the recycled sand from waste demolition, when examined in mortars, revealed that it was more beneficial in lime mortars rather than in stronger lime-pozzolan or lime-pozzolan and cement mortars as a decrease in their performance were recorded in the latter cases due to the mortars’ structure [31]. It seems that two competitive mechanisms acted in these mortars; high porosity (due to high water content and the nature of the aggregates) which assists toward low strength and durability and the chemical reaction due to the presence of reactive components which creates a strong structure. This chemical reaction is a stronger mechanism in the case of lime mortars and prevails in relation to the competitive mechanisms of the higher porosity [32].
In an effort to test different aggregate-related properties to hydraulic lime mortar, Pavia et al. suggested [33] that an increase in the calcite content of the aggregate lowers the flexural and compressive strength of the mortar. At the same time, they proved that sharp aggregate, as well as aggregate with small average particle size, tends to increase the mechanical strength and bulk density of a mortar simultaneously reducing porosity, water absorption, and capillary suction. Additionally, they concluded that aggregates containing particles of a wide size range can increase the mechanical strength and bulk density of the hardened mortar diminishing porosity, water absorption, and capillary suction.
2.2 Different sands in mortars
The role of aggregates on the structure and behavior of lime-based mortars is examined by studying the influence of the aggregate content, type, and grain size on the strength, porosity, and volume stability of the mortars. Trying to understand how the properties of the sands influence important macroscopic properties of pure lime mortar, threes sands that were available in the market were selected and analyzed in the laboratory. All of them were river origins of siliceous content (Figure 3).
Figure 3.
(a) Black sand, (b) yellow sand, and (c) blonde sand.
X-ray diffraction analysis (XRD) using a D2 Phaser 2nd generation, Bruker instruments, indicated that blonde sand was containing quartz, feldspar, magnetite, calcite, hornblende. Yellow sand contained quartz, feldspar, magnetite and black contained quartz, feldspars, biotite, and hornblende. Physical properties, such as water absorption, specific gravity, and sand equivalent (S.E.), are shown in Table 1 while the chemical analysis revealed the silicic nature of the sands (Table 2).
Water absorption %
Specific gravity g/cm3
S.E.
Blonde
0.70
2.36
90.5
Black
1.46
2.35
98.0
Yellow
1.09
2.34
75.0
Table 1.
Physical properties of sands.
Soluble in acids % b.w.
Soluble salts % b.w.
Sample
Na2O
K2O
CaO
MgO
Fe2O3
Al2O3
SiO2
L.I.%
Cl−
NO3−
SO42−
Black sand
3.24
2.63
3.05
1.28
3.66
13.83
68.37
3.45
0.19
0.08
<0.01
Blonde sand
2.97
1.76
3.16
1.87
5.69
14.17
66.00
4.23
0.09
0.11
0.04
Yellow sand
7.82
0.94
0.73
0.46
1.02
18.71
67.14
2.88
0.01
<0.01
0.31
Table 2.
Chemical analysis of sands.
Lime mortars were prepared using lime CL90 (based on EN459) [34] and the compositions were produced, as shown in Table 3. The workability was measured with a flow table as described in EN1015-3 [35].
Composition
Lime
Blond sand
Black sand
Yellow sand
W/B
Workability (cm)
L-blond
1
3
—
—
0.758
15.0
L-black
1
—
3
—
0.800
14.5
L-yellow
1
—
—
3
0.750
14.8
Table 3.
Composition of trial mortar mixtures.
The samples were cured based on EN456 and at 28 days, the compressive strength and the open porosity were recorded (Table 4).
Composition
Compressive strength (MPa)
Porosity % (RILEM CPC11.3)
L-blond
1.14
26.42
L-black
1.03
27.11
L-yellow
0.98
30.79
Table 4.
Properties of the produced mortars at 28 days.
The results show that there are different properties recorded in the produced mortars even when siliceous sands are used. The different properties, such as S.E. and the water absorption capacity of the sand grains, influence both the fresh (workability) and the hardened properties (porosity, strength) of the produced mortars.
The natural sands can be of similar origin with the crushed but weathering not only rounded the particles but also changed the proportions and removed most of the light minerals, such as the flaky micas. Due to these differences, mixtures with crushed sand often display higher water demand and lower workability than the corresponding composite with glaciofluvial sand. Additionally, crushed sand has a positive impact on long-term strength. It seems that, when rough-grained sand is used, strong cohesion with the binder can be achieved, as shown in Figure 4, where mortars with rounded and crushed sand were examined under scanning electron microscopy (SEM) [17].
Figure 4.
SEM examination of rounded sand grain (left) where there is a gap in the contact zone and angular grain with strong cohesion (right).
The mechanical features, particle shape, grading, and physical properties, such as moisture absorption, sand equivalent value, are what can be labeled as properties of interest in the aggregates when used in mortars. Some of these most important properties are shown in Table 5.
Property
Regulation
Determination of rock compressive strength
ASTM C170
Determination of disintegration resistance (health) of aggregates (sodium sulfate method).
ASTM C88 AASHΤO Τ104)
Determination of mineral hardness
the MOHS scale
Determination of specific gravity of aggregates
BS 812/ ΑΑSΗΤΟ T 19.
Determination of moisture absorption of aggregates.
AASHTO Τ85
Determination of granulometric analysis
AASHTO Τ27/ AASHTO T11/ ΕΝ933-1
Determination of ultra-fine crushed material by rinsing
Α8ΤΜ 0117 (AASHTO Τ37)
Determination of equivalent sand
AASHTO Τ176
Determination of abrasion resistance of aggregates
BS 812/75
Determination of wear in aggregate crushing
BS 812/75
Determination of wear on the impact of aggregates
BS 812/75
Determination of plaque index
88,812/75 Section 105.1
Table 5.
Important properties of aggregates to be used in mortar production.
The bond behavior in the interface between the binder and the aggregates has a strong effect on the mortar properties since the effectiveness of the reinforcement provided by the addition of particles depends on the interfacial bond (Figure 5). This is since the size, shape, and content of the particles predominantly control the morphological features of the internal structure of the composite.
Figure 5.
Macroscopic examination of contact zones of natural aggregates and binders in old mortars. Despite the presence of cracks in the binder in the left image, the cohesion is strong. On the right, there are pores on the interface probably due to the high content in aggregates in relation to the binder.
The test results showed that with increasing volume fraction of aggregate, the compressive strength of the composite decreases, which is different from the prediction of conventional composite theories. The possible explanation of this result is based on the interface transition zone (ITZ) around the aggregate, which is the weak zone in composites (Figure 6) [15]. With more aggregate added into the mixtures, more interfaces are formed in the hardened material. The compatibility between the aggregate of the paste affects the development of strong cohesion at the aggregate-matrix interface in many cases and that usually indicates the good performance of the mortar. As aggregates are, by weight or by volume, the major component of mortars, they can be a source of silica, which can react in certain conditions with the binder, leading to the formation of reaction rims at the edge of the grains and recrystallization along with the pre-existing cracks (Figure 7).
Figure 6.
Examination under SEM of natural aggregate and lime binder with weak ITZ (left) and crushed brick as aggregate and lime-pozzolan binder with strong ITZ (right).
Figure 7.
Old mortars under the polarized microscope (x10). Reaction rim in the interfacial zone of the binders and the aggregates used.
Apart from the different types of aggregates as their mineralogy is concerned, the volume content in the mixture, the maximum size, and their gradation influences the structure of a binder—aggregate mixture [3, 5]. The analysis of mortars reveals that higher strength values are attained for lime mortars of low binder/aggregate (B/A) ratio (1:1.5, 1:2.5, and 1:3) which contained sand (0–4 mm). Coarse aggregates have contributed positively to the volume stability of lime mortars. The microstructure has recorded the restriction of volume changes in cases where coarse aggregates have been used in the structure of lime mortars (Figure 8).
Figure 8.
Pores and cracks in the structure of lime mortar with coarse aggregates (polarized microscope, x15).
However, it is well recognized that coarse aggregate particles can act as crack arresters, as they restrict the shrinkage of the binder so that under an increasing load, extra energy is absorbed for the formation of a new crack (Figure 9) [36].
Figure 9.
Cracks inside the binder where they meet the aggregate volume as the obstacle.
3. Designing repair mortars
Usually, a detailed analysis of the authentic building materials is performed to establish an opinion about the materials and techniques used during the construction phase [2]. Based on the results of this analysis, the design and laboratory production of some materials follows [14, 37]. The destructive consequences from the use of incompatible repair materials are related to different physical, chemical, and elastic characteristics that many new materials possess in relation to the old lime-based ones. For this reason, the quality of the materials used in intervention works is of primary importance for the longevity and economy of interventions. However, standard test methods and recommendations have not yet been developed despite the effort at the European level.
As river sand remains as one of the most widely used fine aggregates due to its desirable properties an increased tendency to use and it is observed. With an increase in construction activities, the demand for river sand has also been increasing. As a result, it has been mined at a high rate, depleting its natural resources and causing serious environmental issues. Also, owing to the excess cost of transportation, the natural river sand has become expensive. Hence, industries are shifting to other materials, such as crushed sand. But as the demand for building materials will continue to increase, their sources for crushed sand might also get exhausted. Therefore, there is a need to replace the fine aggregate either completely or partially with an alternative material that can satisfy the properties required for concrete, which is cost-effective and at the same time sustainable. Finding an alternative material to river sand has now become imperative.
The incentive to use sand from building demolition in repairing mortars derives from different needs. Natural sand originating from rivers is becoming rare, while the extraction of aggregates from quarries carries an increased administrative cost due to new strict legislations.
Both practices are not considered environmental friendly and, thus, the criteria and legislation for sand extraction are becoming stricter and demanding, while in some places, good quality natural sands are not available. On the other hand, the increased waste production offers the availability of large volumes of recycled materials and public concern about the environment pushes toward their utilization. The possibility of incorporating fine recycled sand originating from construction and demolition waste in lime-based traditional mortars. The study showed that the recycled sand had an even grain distribution, without any hazardous material and low content of soluble salts [38]. The mortars mixtures with recycled sand showed increased water demand and reduced workability compared to mortars with natural sands, even when superplasticizer was used [39]. The mechanical strength measured at 28 and 90 days showed good results as the mortars with lime and recycled sand had higher compressive strength compared to mortars with natural sands [40].
Additionally, several industrial wastes, (fly ash, demolition waste, slag, glass, brick waste, and plastic), have been shown to be suitable as construction materials and readily follow the design requirements. The substitution of the siliceous aggregate with plastic sand leads to a decrease in mechanical properties, opportunities in the use of these materials are not affected, especially for applications that do not require a structural function [41].
4. Conclusions
The mechanical and physical properties of a mortar both at fresh state, but also long-term, depend on multiple factors, including binder type, curing time, binder—aggregate and binder—water ratios, nature, shape, and grading of aggregates, the compaction degree, and also the environment in which they function. As mortars are composite materials, each component has a special role in the ultimate quality of the material. Aggregates, being of great volume in the mortar mass, significantly influence the structure and the properties achieved in all states of mortar production. The analyses of old mortars revealed the continued presence of sand in the mortars from pre-history up to the cement era. Coarser grains were also used in the technology of mortars. Generally, it is accepted that the strongest mortar mixes are produced from well-graded, clean, and angular aggregates. Usually, they were of local origin following principles of ecology and economy.
The same principles should be applied today having the technological evolution as an alley to protect the environment and work on the benefit of the constructions. Understanding the mechanisms of action and the parameters affecting significant properties in mortars, a well-engineered mixture can be achieved utilizing alternative solutions to protect natural resources and at the same time bring to the market high-quality innovative mortars. Recycled sands are promising materials in construction as after specific tests, they can be utilized either in repairing old structures or even in preparing new cement-based mortars.
\n',keywords:"sand, mortars, composites, mechanical-physical properties",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/80288.pdf",chapterXML:"https://mts.intechopen.com/source/xml/80288.xml",downloadPdfUrl:"/chapter/pdf-download/80288",previewPdfUrl:"/chapter/pdf-preview/80288",totalDownloads:60,totalViews:0,totalCrossrefCites:0,dateSubmitted:"January 4th 2022",dateReviewed:"January 5th 2022",datePrePublished:"February 3rd 2022",datePublished:null,dateFinished:"January 31st 2022",readingETA:"0",abstract:"Mortars are diachronic composite materials used in masonry construction to serve multiple roles. Their durability and esthetic harmonization in constructions of the different eras were the reasons why numerous research works have been realized over recent decades. Each time, the role of the mortars’ components revealed significant pieces of information on the technology used. Despite the indisputable role of the binders on the mortar’s quality, aggregates of different characteristics had a significant role in the behavior of mortars. The addition of aggregates to a binding system in mortars technology has proved to confer technical advantages as they contribute to volume stability, durability, and structural performance. Apart from the different types of aggregates, as their mineralogy and origin are concerned, the volume content in the mixture, the maximum size, and their gradation influences the structure of a binder—aggregate mixture and the performance of mortars overall. In the present article, the diachronic presence of mortars is presented. The role of aggregates is emphasized to understand their impact on the longevity and durability of the mortars.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/80288",risUrl:"/chapter/ris/80288",signatures:"Maria Stefanidou and Parthena Koltsou",book:{id:"11186",type:"book",title:"Sand in Construction",subtitle:null,fullTitle:"Sand in Construction",slug:null,publishedDate:null,bookSignature:"Prof. Sayed Hemeda",coverURL:"https://cdn.intechopen.com/books/images_new/11186.jpg",licenceType:"CC BY 3.0",editedByType:null,isbn:"978-1-80355-586-7",printIsbn:"978-1-80355-585-0",pdfIsbn:"978-1-80355-587-4",isAvailableForWebshopOrdering:!0,editors:[{id:"258282",title:"Prof.",name:"Sayed",middleName:null,surname:"Hemeda",slug:"sayed-hemeda",fullName:"Sayed Hemeda"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:null,sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. The influence of sands in mortar’s properties",level:"1"},{id:"sec_2_2",title:"2.1 Mortars as composite materials",level:"2"},{id:"sec_3_2",title:"2.2 Different sands in mortars",level:"2"},{id:"sec_5",title:"3. Designing repair mortars",level:"1"},{id:"sec_6",title:"4. Conclusions",level:"1"}],chapterReferences:[{id:"B1",body:'Pachta V, Stefanidou M, Konopisi S, Papayianni I. Technological evolution of historic structural mortars. Journal of Civil Engineering and Architecture. 2014;8(7):846-854'},{id:"B2",body:'Papayianni I. Durability lessons from the study of the old mortars and concretes, P.K. Mehta Symposium on Durability of Concrete, Nice, May 23, 1994, Organized by CANMET/ACI, pp. 1-30'},{id:"B3",body:'Martin R. Palladius Traite’ D’Agriculture. Livre I. Societe’ D’Edition ‘Les Belles Letres’; 1976'},{id:"B4",body:'Eichholz, Pliny the Elder. Natural History. London: William Heinemann Ltd., Harvard University Press; 1962. 2nd edition 1971'},{id:"B5",body:'Hicky Morgan M. Vitruvius: The Ten Books on Architecture. Cambridge: Harvard University Press; 1914'},{id:"B6",body:'Palivou K. Akrotiri of Thera: The Constructional Technology. Athens: Library of the Archaeological Society at Athens; 1999. (in Greek)'},{id:"B7",body:'Orlandos AK. The Building Materials of Ancient Greeks, According to Writers, Inscriptions and Monuments: Part A. Vol. 2. Athens: Ancient Greek Architecture; 1958. (in Greek)'},{id:"B8",body:'Bugini R, Salvatori A. Investigation of the characteristics and properties of “Cocciopesto” from the ancient Roman period. Conservation of Stone and Other Materials. 1991;1:386-393'},{id:"B9",body:'Papayianni I, Stefanidou M. Repair mortars for monuments in Byzantine architecture. In: Proceeding of 5th International Congress on Restoration of Architectural Heritage, CICOP, Firenze. 2000. pp. 1671-1683'},{id:"B10",body:'Baronio G, Binda L, Tedeschi C. Thick mortar joints in Byzantine buildings: Study of their composition and mechanical behaviour. In: Proceeding of International Conference on Studies in Ancient Structures, Istanbul. 1997. pp. 235-244'},{id:"B11",body:'Papayianni I, Stefanidou M. Repair mortars suitable for interventions of Ottoman monuments. In: Proceeding of International Conference on Studies in Ancient Structures, Istanbul. 1997. pp. 255-264'},{id:"B12",body:'Papayianni I. Technology of mortars and bricks used in Ottoman monuments in Thessaloniki. In: Arun G, Seçkin N, editors. Proceeding of International Conference on Studies in Ancient Structures, Istanbul. 1997. pp. 245-253'},{id:"B13",body:'Maravelaki-Kalaitzakia P, Bakolas A, Moropoulou A. Physico-chemical study of cretan ancient mortars. Cement and Concrete Research. 2003;33:651-661'},{id:"B14",body:'Papayianni I. Design of compatible repair materials for the restoration of monuments. International Journal for Restoration. 2004;10(6):623-636'},{id:"B15",body:'Lanas J, Alvarez-Galindo JI. Masonry repair lime-based mortars: factors affecting the mechanical behaviour. Cement and Concrete Research. 2003;33(11):1867-1876'},{id:"B16",body:'Stefanidou M. Crushed and river-origin sands used as aggregates in repair mortars. Geosciences. 2016;6(2):23. DOI: 10.3390/geosciences6020023'},{id:"B17",body:'Stefanidou M, Papayianni I. The role of aggregates on the structure and properties of lime mortars cement and concrete composites. 2006;27(9-10):914-919'},{id:"B18",body:'Arulmoly B. Chaminda Konthesingha, Anura Nanayakkara Performance evaluation of cement mortar produced with manufactured sand and offshore sand as alternatives for river sand. Construction and Building Materials. 2021;297:123784'},{id:"B19",body:'Handbook of Materials Selection. Myer Kutz Associates editors. In: Chapter 12 Composite Materials, Carl Zweben. New York: John Wiley & Sons; 2002. pp. 357-386'},{id:"B20",body:'EN 13139:2002 Aggregates for mortar'},{id:"B21",body:'Shin K-J, Lee S-C, Kim YY. Role of fine aggregates on mechanical properties of mortar. Materials Research Innovations. 2015;19(8):S8-690-S8-692. DOI: 10.1179/1432891715Z.0000000001778'},{id:"B22",body:'Santos AR, do Rosário Veiga M, Silva AS, de Brito J, Álvarez JI. Evolution of the microstructure of lime-based mortars and influence on the mechanical behaviour: The role of the aggregates. Construction and Building Materials. 2018;187:907-922'},{id:"B23",body:'Papayianni I, Stefanidou M, Christodoulou S. Influence of the sand fines on the mechanical and physical properties of lime-based renders and plasters. In: Válek J, Groot C, Hughes JJ, editors. Proceedings of the 2nd Conference and of the Final Workshop of RILEM TC 203-RHM, Prague. 2010. pp. 1135-1144'},{id:"B24",body:'Westerholm M, Lagerblad B, Silfwerbrand J, Forssberg Influence E. of fine aggregate characteristics on the rheological properties of mortars. Cement and Concrete Composites. 2008;30(4):274-282'},{id:"B25",body:'EN1015-1:1999 Methods of test for mortar for masonry Part 1: Determination of particle size distribution (by sieve analysis)'},{id:"B26",body:'Amparano FE, Xi Y, Roh Y-S. Experimental study on the effect of aggregate content on fracture behavior of concrete. Engineering Fracture Mechanics. 2000;67:65-84'},{id:"B27",body:'Asamoto S, Ishida T, Maekawa K. Volumetric stability of aggregates and shrinkage of concrete as composites. Journal of Advanced Concrete Technology. 2008;6(1):77-90'},{id:"B28",body:'Chang TP, Taso KL, Lin BR. Effect of aggregate on fracture properties of high-performance concrete. In: Mihashi H, Rokugo K, editors. Fracture mechanics of concrete structures, Proc. of FRAMCOS-3. D-79104 Freiburg: Aedificatio Publishers, 1998. pp. 151-160'},{id:"B29",body:'Neville AM. Properties of Concrete. Fourth ed. New York: John Wiley & Sons Inc.; 1996. pp. 244-248'},{id:"B30",body:'Zhang W, Zakaria M, Hama Y. Influence of aggregate materials characteristics on the drying shrinkage properties of mortar and concrete. Construction and Building Materials. 2013;49:500-510'},{id:"B31",body:'Stefanidou M, Anastasiou E, Filikas KG. Recycled sand in lime-based mortars. Waste Management. 2014;34:2595-2602'},{id:"B32",body:'Zhang J, An X, Nie D. Effect of fine aggregate characteristics on the thresholds of self-compacting paste rheological properties. Construction and Building Materials. 2016;116(30):355-365'},{id:"B33",body:'Pavıa S, Toomey B. Influence of the aggregate quality on the physical properties of natural feebly-hydraulic lime mortars. Materials and Structures. 2008;41:559-569. DOI: 10.1617/s11527-007-9267-4'},{id:"B34",body:'EN 459-2:2010 Building lime – Part 2: Test methods'},{id:"B35",body:'EN1014-3 Part 3:1999 Determination of consistence of fresh mortar (by flow table)'},{id:"B36",body:'Lea FM. The Chemistry of Cement and Concrete. London: Edward Arnold Ltd; 1970. pp. 406-559'},{id:"B37",body:'Moropoulou A, Bakolas A, Moundoulas P, Aggelakopoulou E, Anagnostopoulou S. Strength development and lime reaction in mortars for repairing historic masonries. Cement and Concrete Composites. 2005;27(2):289-294'},{id:"B38",body:'Martínez I, Etxeberria M, Pavón E, Díaz N. A comparative analysis of the properties of recycled and natural aggregate in masonry mortars. Construction and Building Materials. 2013;49:384-392'},{id:"B39",body:'Anastasiou E, Georgiadis-Filikas K, Stefanidou M. Utilization of fine recycled aggregates in concrete with fly ash and steel slag. Construction and Building Materials. 2014;50:154-161'},{id:"B40",body:'Jiménez JR, Ayuso J, López M, Fernández JM, de Brito J. Use of fine recycled aggregates from ceramic waste in masonry mortar manufacturing. Construction and Building Materials. 2013;40:679-690'},{id:"B41",body:'Iucolano F, Liguori B, Caputo D, Colangelo F, Cioffi R. Recycled plastic aggregate in mortars composition: Effect on physical and mechanical properties. Materials & Design. 2013;52:916-922'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Maria Stefanidou",address:"stefan@civil.auth.gr",affiliation:'
Laboratory of Building Materials, School of Civil Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece
Laboratory of Building Materials, School of Civil Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece
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For propylene-based polyolefin, various polyolefins with distinctive characteristic of mechanical and optical properties were made with specific catalysts in commercial industries owned especially by those large worldwide companies. In this chapter, Ziegler-Natta catalysts, metallocene catalysts, and post-metallocene catalysts for PP polymerization are discussed in detail. Gas phase, bulk, slurry, and solution polymerization processes, such as Spheripol (Basell), Hypol (Mitsui Chemicals), Unipol (Dow Chemical), Innovene (INEOS), Novelen (BASF), Spherizone (Basell), and Borstar (Borealis), developed by the industrial tycoons were reviewed. The molecular architecture of the PP-based polyolefins could be tailored precisely using specific high-performance catalyst in an appropriate polymerization process, and different types of PPs, including homopolypropylene (HPP), random copolypropylene (RPP), impact PP, PP-based block copolymer, functionalized PP, etc., are produced. The relationship between molecular structure and performance of the PP-based polyolefins is also discussed thereof.",signatures:"Xiong Wang, Xiaoyu Han and Renwei Xu",authors:[{id:"286587",title:"Dr.",name:"Xiong",surname:"Wang",fullName:"Xiong Wang",slug:"xiong-wang",email:"wangxiong2011@lzu.edu.cn"},{id:"287798",title:"Ms.",name:"Xiaoyu",surname:"Han",fullName:"Xiaoyu Han",slug:"xiaoyu-han",email:"hanxiaoyu@petrochina.com.cn"},{id:"298804",title:"Dr.",name:"Renwei",surname:"Xu",fullName:"Renwei Xu",slug:"renwei-xu",email:"xurenwei@petrochina.com.cn"}],book:{id:"7195",title:"Polypropylene",slug:"polypropylene-polymerization-and-characterization-of-mechanical-and-thermal-properties",productType:{id:"1",title:"Edited Volume"}}}],collaborators:[{id:"21740",title:"Prof.",name:"Domenico",surname:"Acierno",slug:"domenico-acierno",fullName:"Domenico Acierno",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Naples Federico II",institutionURL:null,country:{name:"Italy"}}},{id:"216922",title:"Dr.",name:"Koh-Hei",surname:"Nitta",slug:"koh-hei-nitta",fullName:"Koh-Hei Nitta",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"281099",title:"Ph.D.",name:"Antonella",surname:"Patti",slug:"antonella-patti",fullName:"Antonella Patti",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Naples Federico II",institutionURL:null,country:{name:"Italy"}}},{id:"281147",title:"Prof.",name:"Luca",surname:"Fambri",slug:"luca-fambri",fullName:"Luca Fambri",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Trento",institutionURL:null,country:{name:"Italy"}}},{id:"281148",title:"Prof.",name:"Luca",surname:"Lutterotti",slug:"luca-lutterotti",fullName:"Luca Lutterotti",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Trento",institutionURL:null,country:{name:"Italy"}}},{id:"286587",title:"Dr.",name:"Xiong",surname:"Wang",slug:"xiong-wang",fullName:"Xiong Wang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"287505",title:"Prof.",name:"Valeria",surname:"Pettarin",slug:"valeria-pettarin",fullName:"Valeria Pettarin",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"295752",title:"Dr.",name:"Alejandra",surname:"Costantino",slug:"alejandra-costantino",fullName:"Alejandra Costantino",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"295755",title:"Mrs.",name:"Caren",surname:"Rosales",slug:"caren-rosales",fullName:"Caren Rosales",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"298804",title:"Dr.",name:"Renwei",surname:"Xu",slug:"renwei-xu",fullName:"Renwei Xu",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null}]},generic:{page:{slug:"open-access-funding",title:"Open Access Funding",intro:"
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The literature source was Web of Science and SSCI, SCI-EXPANDED, A&HCI, CPCI-S, CPCI-SSH, and ESCI indexes. Fifty-two articles were reviewed; however, 14 of them were not been included in the study. As a result, 38 articles were examined. Level of education, field of education, and material types of AR used in education and reported educational advantages of AR have been investigated. All articles are categorized according to target groups, which are early childhood education, primary education, secondary education, high school education, graduate education, and others. AR technology has been mostly carried out in primary and graduate education. “Science education” is the most explored field of education. Mobile applications and marker-based materials on paper have been mostly preferred. 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The more active a lesson, the more students tend to engage intellectually and emotionally in the learning activities. Cooperative learning is the foundation on which many of the active learning procedures are based. Cooperative learning is the instructional use of small groups so that students work together to maximize their own and each other’s learning. Most of the active learning procedures, such as problem-based learning, team-learning, collaborative learning, and PALS, require that students work cooperatively in small groups to achieve joint learning goals. Cooperative learning is based on two theories: Structure-Process-Outcome theory and Social Interdependence theory. Four types of cooperative learning have been derived: formal cooperative learning, informal cooperative learning, cooperative base groups, and constructive controversy. There is considerable research confirming the effectiveness of cooperative learning. 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Currently, you can hear a lot of criticism that the classroom has not changed significantly compared to the last century or even like two centuries ago. Do the teachers succeed in modern changes? The purpose of the chapter is to summarize the current changes in didactics for the use of innovative teaching methods and study the understanding of changes by teachers. In this chapter, we consider four areas: the expansion of the subject of pedagogy, environmental approach to teaching, the digital generation and the changes taking place, and innovation in teaching. The theory of education, figuratively speaking, has two levels. At the macro-level, in the “education-society” relationship, decentralization and diversification, internationalization of education, and the introduction of digital technologies occur. At the micro-level in the “teacher-learner” relationship, there is an active mix of traditional and innovative methods, combination of an activity approach with an energy-informational environment approach, cognition with constructivism and connectivism.",book:{id:"5980",slug:"new-pedagogical-challenges-in-the-21st-century-contributions-of-research-in-education",title:"New Pedagogical Challenges in the 21st Century",fullTitle:"New Pedagogical Challenges in the 21st Century - Contributions of Research in Education"},signatures:"Aigerim Mynbayeva, Zukhra Sadvakassova and Bakhytkul\nAkshalova",authors:[{id:"201997",title:"Dr.",name:"Aigerim",middleName:null,surname:"Mynbayeva",slug:"aigerim-mynbayeva",fullName:"Aigerim Mynbayeva"},{id:"209208",title:"Dr.",name:"Zukhra",middleName:null,surname:"Sadvakassova",slug:"zukhra-sadvakassova",fullName:"Zukhra Sadvakassova"},{id:"209210",title:"Dr.",name:"Bakhytkul",middleName:null,surname:"Akshalova",slug:"bakhytkul-akshalova",fullName:"Bakhytkul Akshalova"}]},{id:"59468",doi:"10.5772/intechopen.74344",title:"Virtual and Augmented Reality: New Frontiers for Clinical Psychology",slug:"virtual-and-augmented-reality-new-frontiers-for-clinical-psychology",totalDownloads:2364,totalCrossrefCites:13,totalDimensionsCites:21,abstract:"In the last decades, the applied approach for the use of virtual reality (VR) and augmented reality (AR) on clinical and health psychology has grown exponentially. These technologies have been used to treat several mental disorders, for example, phobias, stress-related disorders, depression, eating disorders, and chronic pain. The importance of VR/AR for the mental health field comes from three main concepts: (1) VR/AR as an imaginal technology, people can feel “as if they are” in a reality that does not exist in external world; (2) VR/AR as an embodied technology, the experience to feel user’s body inside the virtual environment; and (3) VR/AR as connectivity technology, the “end of geography’. In this chapter, we explore the opportunities provided by VR/AR as technologies to improve people’s quality of life and to discuss new frontiers for their application in mental health and psychological well-being promotion.",book:{id:"6543",slug:"state-of-the-art-virtual-reality-and-augmented-reality-knowhow",title:"State of the Art Virtual Reality and Augmented Reality Knowhow",fullTitle:"State of the Art Virtual Reality and Augmented Reality Knowhow"},signatures:"Sara Ventura, Rosa M. Baños and Cristina Botella",authors:[{id:"106036",title:"Dr.",name:"Rosa Maria",middleName:null,surname:"Baños",slug:"rosa-maria-banos",fullName:"Rosa Maria Baños"},{id:"227763",title:"Ph.D.",name:"Sara",middleName:null,surname:"Ventura",slug:"sara-ventura",fullName:"Sara Ventura"},{id:"229056",title:"Dr.",name:"Cristina",middleName:null,surname:"Botella",slug:"cristina-botella",fullName:"Cristina Botella"}]},{id:"64583",doi:"10.5772/intechopen.81714",title:"Evaluating a Course for Teaching Advanced Programming Concepts with Scratch to Preservice Kindergarten Teachers: A Case Study in Greece",slug:"evaluating-a-course-for-teaching-advanced-programming-concepts-with-scratch-to-preservice-kindergart",totalDownloads:1422,totalCrossrefCites:13,totalDimensionsCites:18,abstract:"Coding is a new literacy for the twenty-first century, and as a literacy, coding enables new ways of thinking and new ways of communicating and expressing ideas, as well as new ways of civic participation. A growing number of countries, in Europe and beyond, have established clear policies and frameworks for introducing computational thinking (CT) and computer programming to young children. In this chapter, we discuss a game-based approach to coding education for preservice kindergarten teachers using Scratch. The aim of using Scratch was to excite students’ interest and familiarize them with the basics of programming in an open-ended, project-based, and personally meaningful environment for a semester course in the Department of Preschool Education in the University of Crete. For 13 weeks, students were introduced to the main Scratch concepts and, afterward, were asked to prepare their projects. For the projects, they were required to design their own interactive stories to teach certain concepts about mathematics or physical science to preschool-age students. The results we obtained were more satisfactory than expected and, in some regards, encouraging if one considers the fact that the research participants had no prior experiences with computational thinking.",book:{id:"6936",slug:"early-childhood-education",title:"Early Childhood Education",fullTitle:"Early Childhood Education"},signatures:"Stamatios Papadakis and Michail Kalogiannakis",authors:null}],mostDownloadedChaptersLast30Days:[{id:"58060",title:"Pedagogy of the Twenty-First Century: Innovative Teaching Methods",slug:"pedagogy-of-the-twenty-first-century-innovative-teaching-methods",totalDownloads:8832,totalCrossrefCites:17,totalDimensionsCites:23,abstract:"In the twenty-first century, significant changes are occurring related to new scientific discoveries, informatization, globalization, the development of astronautics, robotics, and artificial intelligence. This century is called the age of digital technologies and knowledge. How is the school changing in the new century? How does learning theory change? Currently, you can hear a lot of criticism that the classroom has not changed significantly compared to the last century or even like two centuries ago. Do the teachers succeed in modern changes? The purpose of the chapter is to summarize the current changes in didactics for the use of innovative teaching methods and study the understanding of changes by teachers. In this chapter, we consider four areas: the expansion of the subject of pedagogy, environmental approach to teaching, the digital generation and the changes taking place, and innovation in teaching. The theory of education, figuratively speaking, has two levels. At the macro-level, in the “education-society” relationship, decentralization and diversification, internationalization of education, and the introduction of digital technologies occur. At the micro-level in the “teacher-learner” relationship, there is an active mix of traditional and innovative methods, combination of an activity approach with an energy-informational environment approach, cognition with constructivism and connectivism.",book:{id:"5980",slug:"new-pedagogical-challenges-in-the-21st-century-contributions-of-research-in-education",title:"New Pedagogical Challenges in the 21st Century",fullTitle:"New Pedagogical Challenges in the 21st Century - Contributions of Research in Education"},signatures:"Aigerim Mynbayeva, Zukhra Sadvakassova and Bakhytkul\nAkshalova",authors:[{id:"201997",title:"Dr.",name:"Aigerim",middleName:null,surname:"Mynbayeva",slug:"aigerim-mynbayeva",fullName:"Aigerim Mynbayeva"},{id:"209208",title:"Dr.",name:"Zukhra",middleName:null,surname:"Sadvakassova",slug:"zukhra-sadvakassova",fullName:"Zukhra Sadvakassova"},{id:"209210",title:"Dr.",name:"Bakhytkul",middleName:null,surname:"Akshalova",slug:"bakhytkul-akshalova",fullName:"Bakhytkul Akshalova"}]},{id:"61746",title:"Facilitation of Teachers’ Professional Development through Principals’ Instructional Supervision and Teachers’ Knowledge- Management Behaviors",slug:"facilitation-of-teachers-professional-development-through-principals-instructional-supervision-and-t",totalDownloads:3384,totalCrossrefCites:2,totalDimensionsCites:2,abstract:"With the rise of global competition and the focus on teacher quality, teacher professional development is becoming increasingly crucial, and the stress and challenges for principals are more severe than ever. Teachers can improve their professional abilities through principals’ instructional supervision and their own knowledge-management (KM) behaviors to benefit students. Thus, this chapter analyzes the relationship among principals’ instructional supervision, teachers’ KM, and teachers’ professional development. The author believes that principals’ instructional supervision and effective KM can facilitate the professional development of teachers. The author also believes the readers can know the relationships among them, and teachers’ professional development can be improved through principal’s instructional supervision and teachers’ KM behaviors.",book:{id:"6674",slug:"contemporary-pedagogies-in-teacher-education-and-development",title:"Contemporary Pedagogies in Teacher Education and Development",fullTitle:"Contemporary Pedagogies in Teacher Education and Development"},signatures:"Chien-Chin Chen",authors:[{id:"232569",title:"Ph.D.",name:"Chien Chih",middleName:null,surname:"Chen",slug:"chien-chih-chen",fullName:"Chien Chih Chen"}]},{id:"75908",title:"From the Classroom into Virtual Learning Environments: Essential Knowledge, Competences, Skills and Pedagogical Strategies for the 21st Century Teacher Education in Kenya",slug:"from-the-classroom-into-virtual-learning-environments-essential-knowledge-competences-skills-and-ped",totalDownloads:519,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"As teachers in Kenya begin to migrate from the classroom to virtual learning spaces following COVID 19 pandemic, there is pressing need to realign Teacher Education to requisite Knowledge, competences, skills, and attitudes that will support online teaching. This chapter explores these needs using a combination of lived experiences and literature review that captured a meta-analysis of research trends on e-learning. While trends in Teacher Education indicate progression towards adoption of technology, there are disparities between the theory and practice. Evidence from recent research and reports; and the recollected experiences confirmed knowledge, competence, skills and pedagogical gaps in the implementation of online learning, that have been exacerbated by COVID-19. The researcher recommends that teacher education should sensitize and train teacher trainees on how to access, analyze and use new knowledge emerging with technology; they also should be coached on how learners learn with technology and on fundamentals of the communication process. Particularly the course on educational technology, should focus on how to create and manage online courses. The 5-stage E-Moderator Model and Universal Design for Learning (UDL) are recommended as effective pedagogical scaffold for online teaching.",book:{id:"10229",slug:"teacher-education-in-the-21st-century-emerging-skills-for-a-changing-world",title:"Teacher Education in the 21st Century",fullTitle:"Teacher Education in the 21st Century - Emerging Skills for a Changing World"},signatures:"Catherine Adhiambo Amimo",authors:[{id:"333482",title:"Dr.",name:"Catherine Adhiambo",middleName:null,surname:"Amimo",slug:"catherine-adhiambo-amimo",fullName:"Catherine Adhiambo Amimo"}]},{id:"75224",title:"Decoding the Digital Gap in Teacher Education: Three Perspectives across the Globe",slug:"decoding-the-digital-gap-in-teacher-education-three-perspectives-across-the-globe",totalDownloads:589,totalCrossrefCites:0,totalDimensionsCites:4,abstract:"Educational use of technology is regularly assessed, and results often show a gap between educational policies and what is actually practiced. This chapter will help clarify how teacher educators experience the changing educational contexts due to the digital revolution, how their meaning-making shifts, and how outside forces influence those processes. The results are based on comparative international studies. Central for this study is practitioners’ professional digital competence, their attitudes towards digital technology and the use of digital technology in education. We found that the influence and contribution of digital practice is carried out quite differently across the globe. Our research questions were: How do practitioners experience teaching in a rapidly changing context? How do attitudes change due to top-down governing of education? and What motivates teacher educators to implement digital technology?",book:{id:"10229",slug:"teacher-education-in-the-21st-century-emerging-skills-for-a-changing-world",title:"Teacher Education in the 21st Century",fullTitle:"Teacher Education in the 21st Century - Emerging Skills for a Changing World"},signatures:"Steinar Thorvaldsen and Siri Sollied Madsen",authors:[{id:"332624",title:"Associate Prof.",name:"Siri Sollied",middleName:null,surname:"Madsen",slug:"siri-sollied-madsen",fullName:"Siri Sollied Madsen"},{id:"332626",title:"Prof.",name:"Steinar",middleName:null,surname:"Thorvaldsen",slug:"steinar-thorvaldsen",fullName:"Steinar Thorvaldsen"}]},{id:"75416",title:"Self-Study Research: Challenges and Opportunities in Teacher Education",slug:"self-study-research-challenges-and-opportunities-in-teacher-education",totalDownloads:777,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"This article aims to describe what self-study research is, why self-study can be a good approach to teacher educators’ professional development and improvements in practice and highlight some challenges and opportunities in this research approach. In addition, the article will shed light on some methodological aspects related to self-study. Self-study refers to teacher educators who in an intentionally and systematically way examine their practice to improve it, based on a deeper understanding of practice, as well as the context practice takes place. In the article, I argue that engaging in self-study is a learning and development process and an approach to developing personal professionalism, collective professionalism and improvements in practice.",book:{id:"10229",slug:"teacher-education-in-the-21st-century-emerging-skills-for-a-changing-world",title:"Teacher Education in the 21st Century",fullTitle:"Teacher Education in the 21st Century - Emerging Skills for a Changing World"},signatures:"Kåre Hauge",authors:[{id:"332053",title:"Associate Prof.",name:"Kåre",middleName:null,surname:"Hauge",slug:"kare-hauge",fullName:"Kåre Hauge"}]}],onlineFirstChaptersFilter:{topicId:"265",limit:6,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:8,limit:8,total:0},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:90,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:108,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:33,numberOfPublishedChapters:330,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:14,numberOfPublishedChapters:145,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:9,numberOfPublishedChapters:140,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:123,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:112,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:22,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:11,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:"2753-6580",doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}},{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}}]},series:{item:{id:"11",title:"Biochemistry",doi:"10.5772/intechopen.72877",issn:"2632-0983",scope:"Biochemistry, the study of chemical transformations occurring within living organisms, impacts all areas of life sciences, from molecular crystallography and genetics to ecology, medicine, and population biology. Biochemistry examines macromolecules - proteins, nucleic acids, carbohydrates, and lipids – and their building blocks, structures, functions, and interactions. Much of biochemistry is devoted to enzymes, proteins that catalyze chemical reactions, enzyme structures, mechanisms of action and their roles within cells. Biochemistry also studies small signaling molecules, coenzymes, inhibitors, vitamins, and hormones, which play roles in life processes. Biochemical experimentation, besides coopting classical chemistry methods, e.g., chromatography, adopted new techniques, e.g., X-ray diffraction, electron microscopy, NMR, radioisotopes, and developed sophisticated microbial genetic tools, e.g., auxotroph mutants and their revertants, fermentation, etc. More recently, biochemistry embraced the ‘big data’ omics systems. Initial biochemical studies have been exclusively analytic: dissecting, purifying, and examining individual components of a biological system; in the apt words of Efraim Racker (1913 –1991), “Don’t waste clean thinking on dirty enzymes.” Today, however, biochemistry is becoming more agglomerative and comprehensive, setting out to integrate and describe entirely particular biological systems. The ‘big data’ metabolomics can define the complement of small molecules, e.g., in a soil or biofilm sample; proteomics can distinguish all the comprising proteins, e.g., serum; metagenomics can identify all the genes in a complex environment, e.g., the bovine rumen. 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Dr. Blumenberg’s research is focused on the epidermis, expression of keratin genes, transcription profiling, keratinocyte differentiation, inflammatory diseases and cancers, and most recently the effects of the microbiome on the skin. He has published more than 100 peer-reviewed research articles and graduated numerous Ph.D. and postdoctoral students.",institutionString:null,institution:{name:"New York University Langone Medical Center",institutionURL:null,country:{name:"United States of America"}}},editorTwo:null,editorThree:null},subseries:{paginationCount:2,paginationItems:[{id:"89",title:"Education",coverUrl:"https://cdn.intechopen.com/series_topics/covers/89.jpg",isOpenForSubmission:!1,annualVolume:null,editor:{id:"260066",title:"Associate Prof.",name:"Michail",middleName:null,surname:"Kalogiannakis",slug:"michail-kalogiannakis",fullName:"Michail Kalogiannakis",profilePictureURL:"https://mts.intechopen.com/storage/users/260066/images/system/260066.jpg",biography:"Michail Kalogiannakis is an Associate Professor of the Department of Preschool Education, University of Crete, and an Associate Tutor at School of Humanities at the Hellenic Open University. He graduated from the Physics Department of the University of Crete and continued his post-graduate studies at the University Paris 7-Denis Diderot (D.E.A. in Didactic of Physics), University Paris 5-René Descartes-Sorbonne (D.E.A. in Science Education) and received his Ph.D. degree at the University Paris 5-René Descartes-Sorbonne (PhD in Science Education). His research interests include science education in early childhood, science teaching and learning, e-learning, the use of ICT in science education, games simulations, and mobile learning. He has published over 120 articles in international conferences and journals and has served on the program committees of numerous international conferences.",institutionString:"University of Crete",institution:{name:"University of Crete",institutionURL:null,country:{name:"Greece"}}},editorTwo:{id:"422488",title:"Dr.",name:"Maria",middleName:null,surname:"Ampartzaki",slug:"maria-ampartzaki",fullName:"Maria Ampartzaki",profilePictureURL:"https://mts.intechopen.com/storage/users/422488/images/system/422488.jpg",biography:"Dr Maria Ampartzaki is an Assistant Professor in Early Childhood Education in the Department of Preschool Education at the University of Crete. Her research interests include ICT in education, science education in the early years, inquiry-based and art-based learning, teachers’ professional development, action research, and the Pedagogy of Multiliteracies, among others. She has run and participated in several funded and non-funded projects on the teaching of Science, Social Sciences, and ICT in education. She also has the experience of participating in five Erasmus+ projects.",institutionString:"University of Crete",institution:{name:"University of Crete",institutionURL:null,country:{name:"Greece"}}},editorThree:null},{id:"90",title:"Human Development",coverUrl:"https://cdn.intechopen.com/series_topics/covers/90.jpg",isOpenForSubmission:!0,annualVolume:11974,editor:{id:"191040",title:"Dr.",name:"Tal",middleName:null,surname:"Dotan Ben-Soussan",slug:"tal-dotan-ben-soussan",fullName:"Tal Dotan Ben-Soussan",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSBf1QAG/Profile_Picture_2022-03-18T07:56:11.jpg",biography:"Tal Dotan Ben-Soussan, Ph.D., is the director of the Research Institute for Neuroscience, Education and Didactics (RINED) – Paoletti Foundation. Ben-Soussan leads international studies on training and neuroplasticity from neurophysiological and psychobiological perspectives. As a neuroscientist and bio-psychologist, she has published numerous articles on neuroplasticity, movement and meditation. She acts as an editor and reviewer in several renowned journals and coordinates international conferences integrating theoretical, methodological and practical approaches on various topics, such as silence, logics and neuro-education. She lives in Assisi, Italy.",institutionString:"Research Institute for Neuroscience, Education and Didactics, Patrizio Paoletti Foundation",institution:null},editorTwo:null,editorThree:null}]},overviewPageOFChapters:{paginationCount:11,paginationItems:[{id:"83075",title:"Practices and Challenges of Community Services at Debre Markos University, Ethiopia: A Case Study",doi:"10.5772/intechopen.105896",signatures:"Adane Mengist",slug:"practices-and-challenges-of-community-services-at-debre-markos-university-ethiopia-a-case-study",totalDownloads:1,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Corporate Social Responsibility",coverURL:"https://cdn.intechopen.com/books/images_new/11602.jpg",subseries:{id:"86",title:"Business and Management"}}},{id:"82858",title:"Corporate Social Responsibility a Case of the Provision of Recreational Facilities",doi:"10.5772/intechopen.105608",signatures:"Peter Musa Wash, Shida Irwana Omar, Badaruddin Mohamed and Mohd Ismail Isa",slug:"corporate-social-responsibility-a-case-of-the-provision-of-recreational-facilities",totalDownloads:5,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Corporate Social Responsibility",coverURL:"https://cdn.intechopen.com/books/images_new/11602.jpg",subseries:{id:"86",title:"Business and Management"}}},{id:"82786",title:"Discussion of Purchasing Virtual Digital Nature and Tourism",doi:"10.5772/intechopen.105869",signatures:"Hiroko Oe and Yasuyuki Yamaoka",slug:"discussion-of-purchasing-virtual-digital-nature-and-tourism",totalDownloads:6,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"A New Era of Consumer Behavior - Beyond the Pandemic",coverURL:"https://cdn.intechopen.com/books/images_new/11581.jpg",subseries:{id:"88",title:"Marketing"}}},{id:"82289",title:"Consumer Culture and Abundance of Choices: Having More, Feeling Blue",doi:"10.5772/intechopen.105607",signatures:"Ondřej Roubal",slug:"consumer-culture-and-abundance-of-choices-having-more-feeling-blue",totalDownloads:7,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"A New Era of Consumer Behavior - Beyond the Pandemic",coverURL:"https://cdn.intechopen.com/books/images_new/11581.jpg",subseries:{id:"88",title:"Marketing"}}}]},overviewPagePublishedBooks:{paginationCount:1,paginationItems:[{type:"book",id:"11392",title:"Leadership in a Changing World",subtitle:"A Multidimensional Perspective",coverURL:"https://cdn.intechopen.com/books/images_new/11392.jpg",slug:"leadership-in-a-changing-world-a-multidimensional-perspective",publishedDate:"May 11th 2022",editedByType:"Edited by",bookSignature:"Muhammad Mohiuddin, Bilal Khalid, Md. 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That is exactly what he does, diving into Machine Learning algorithms and technologies to help TECNALIA to decide whether something is great in theory or will actually impact on the product or processes of its projects. So, he is expert at framing experiments, developing hypotheses, and proving whether they’re true or not, in order to investigate fundamental problems with a longer time horizon. He is also able to design and develop PoCs and system prototypes in simulation. He has participated in several national and internacional R&D projects.\n\nAs another relevant part of his everyday research work, he usually publishes his findings in reputed scientific refereed journals and international conferences, occasionally acting as reviewer and Programme Commitee member. Concretely, since 2018 he has published 9 JCR (8 Q1) journal papers, 9 conference papers (e.g. ECML PKDD 2021), and he has co-edited a book. He is also active in popular science writing data science stories for reputed blogs (KDNuggets, TowardsDataScience, Naukas). Besides, he has recently embarked on mentoring programmes as mentor, and has also worked as data science trainer.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"103779",title:"Prof.",name:"Yalcin",middleName:null,surname:"Isler",slug:"yalcin-isler",fullName:"Yalcin Isler",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRyQ8QAK/Profile_Picture_1628834958734",biography:"Yalcin Isler (1971 - Burdur / Turkey) received the B.Sc. degree in the Department of Electrical and Electronics Engineering from Anadolu University, Eskisehir, Turkey, in 1993, the M.Sc. degree from the Department of Electronics and Communication Engineering, Suleyman Demirel University, Isparta, Turkey, in 1996, the Ph.D. degree from the Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey, in 2009, and the Competence of Associate Professorship from the Turkish Interuniversity Council in 2019.\n\nHe was Lecturer at Burdur Vocational School in Suleyman Demirel University (1993-2000, Burdur / Turkey), Software Engineer (2000-2002, Izmir / Turkey), Research Assistant in Bulent Ecevit University (2002-2003, Zonguldak / Turkey), Research Assistant in Dokuz Eylul University (2003-2010, Izmir / Turkey), Assistant Professor at the Department of Electrical and Electronics Engineering in Bulent Ecevit University (2010-2012, Zonguldak / Turkey), Assistant Professor at the Department of Biomedical Engineering in Izmir Katip Celebi University (2012-2019, Izmir / Turkey). He is an Associate Professor at the Department of Biomedical Engineering at Izmir Katip Celebi University, Izmir / Turkey, since 2019. In addition to academics, he has also founded Islerya Medical and Information Technologies Company, Izmir / Turkey, since 2017.\n\nHis main research interests cover biomedical signal processing, pattern recognition, medical device design, programming, and embedded systems. He has many scientific papers and participated in several projects in these study fields. He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014) and has been IEEE Senior Member since 2014.",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"310576",title:"Prof.",name:"Erick Giovani",middleName:null,surname:"Sperandio Nascimento",slug:"erick-giovani-sperandio-nascimento",fullName:"Erick Giovani Sperandio Nascimento",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y00002pDKxDQAW/ProfilePicture%202022-06-20%2019%3A57%3A24.788",biography:"Prof. Erick Sperandio is the Lead Researcher and professor of Artificial Intelligence (AI) at SENAI CIMATEC, Bahia, Brazil, also working with Computational Modeling (CM) and HPC. He holds a PhD in Environmental Engineering in the area of Atmospheric Computational Modeling, a Master in Informatics in the field of Computational Intelligence and Graduated in Computer Science from UFES. He currently coordinates, leads and participates in R&D projects in the areas of AI, computational modeling and supercomputing applied to different areas such as Oil and Gas, Health, Advanced Manufacturing, Renewable Energies and Atmospheric Sciences, advising undergraduate, master's and doctoral students. He is the Lead Researcher at SENAI CIMATEC's Reference Center on Artificial Intelligence. In addition, he is a Certified Instructor and University Ambassador of the NVIDIA Deep Learning Institute (DLI) in the areas of Deep Learning, Computer Vision, Natural Language Processing and Recommender Systems, and Principal Investigator of the NVIDIA/CIMATEC AI Joint Lab, the first in Latin America within the NVIDIA AI Technology Center (NVAITC) worldwide program. He also works as a researcher at the Supercomputing Center for Industrial Innovation (CS2i) and at the SENAI Institute of Innovation for Automation (ISI Automação), both from SENAI CIMATEC. He is a member and vice-coordinator of the Basic Board of Scientific-Technological Advice and Evaluation, in the area of Innovation, of the Foundation for Research Support of the State of Bahia (FAPESB). He serves as Technology Transfer Coordinator and one of the Principal Investigators at the National Applied Research Center in Artificial Intelligence (CPA-IA) of SENAI CIMATEC, focusing on Industry, being one of the six CPA-IA in Brazil approved by MCTI / FAPESP / CGI.br. He also participates as one of the representatives of Brazil in the BRICS Innovation Collaboration Working Group on HPC, ICT and AI. He is the coordinator of the Work Group of the Axis 5 - Workforce and Training - of the Brazilian Strategy for Artificial Intelligence (EBIA), and member of the MCTI/EMBRAPII AI Innovation Network Training Committee. He is the coordinator, by SENAI CIMATEC, of the Artificial Intelligence Reference Network of the State of Bahia (REDE BAH.IA). He leads the working group of experts representing Brazil in the Global Partnership on Artificial Intelligence (GPAI), on the theme \"AI and the Pandemic Response\".",institutionString:"Manufacturing and Technology Integrated Campus – SENAI CIMATEC",institution:null},{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:'"Politechnica" University Timişoara',institution:null},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of TECNALIA’s ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes (resource planning, energy efficiency, etc).",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"278948",title:"Dr.",name:"Carlos Pedro",middleName:null,surname:"Gonçalves",slug:"carlos-pedro-goncalves",fullName:"Carlos Pedro Gonçalves",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRcmyQAC/Profile_Picture_1564224512145",biography:'Carlos Pedro Gonçalves (PhD) is an Associate Professor at Lusophone University of Humanities and Technologies and a researcher on Complexity Sciences, Quantum Technologies, Artificial Intelligence, Strategic Studies, Studies in Intelligence and Security, FinTech and Financial Risk Modeling. He is also a progammer with programming experience in:\n\nA) Quantum Computing using Qiskit Python module and IBM Quantum Experience Platform, with software developed on the simulation of Quantum Artificial Neural Networks and Quantum Cybersecurity;\n\nB) Artificial Intelligence and Machine learning programming in Python;\n\nC) Artificial Intelligence, Multiagent Systems Modeling and System Dynamics Modeling in Netlogo, with models developed in the areas of Chaos Theory, Econophysics, Artificial Intelligence, Classical and Quantum Complex Systems Science, with the Econophysics models having been cited worldwide and incorporated in PhD programs by different Universities.\n\nReceived an Arctic Code Vault Contributor status by GitHub, due to having developed open source software preserved in the \\"Arctic Code Vault\\" for future generations (https://archiveprogram.github.com/arctic-vault/), with the Strategy Analyzer A.I. module for decision making support (based on his PhD thesis, used in his Classes on Decision Making and in Strategic Intelligence Consulting Activities) and QNeural Python Quantum Neural Network simulator also preserved in the \\"Arctic Code Vault\\", for access to these software modules see: https://github.com/cpgoncalves. He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. Science CV available at: https://www.cienciavitae.pt//pt/8E1C-A8B3-78C5 and ORCID: https://orcid.org/0000-0002-0298-3974',institutionString:"University of Lisbon",institution:{name:"Universidade Lusófona",country:{name:"Portugal"}}},{id:"241400",title:"Prof.",name:"Mohammed",middleName:null,surname:"Bsiss",slug:"mohammed-bsiss",fullName:"Mohammed Bsiss",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241400/images/8062_n.jpg",biography:null,institutionString:null,institution:null},{id:"276128",title:"Dr.",name:"Hira",middleName:null,surname:"Fatima",slug:"hira-fatima",fullName:"Hira Fatima",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/276128/images/14420_n.jpg",biography:"Dr. Hira Fatima\nAssistant Professor\nDepartment of Mathematics\nInstitute of Applied Science\nMangalayatan University, Aligarh\nMobile: no : 8532041179\nhirafatima2014@gmal.com\n\nDr. Hira Fatima has received his Ph.D. degree in pure Mathematics from Aligarh Muslim University, Aligarh India. Currently working as an Assistant Professor in the Department of Mathematics, Institute of Applied Science, Mangalayatan University, Aligarh. She taught so many courses of Mathematics of UG and PG level. Her research Area of Expertise is Functional Analysis & Sequence Spaces. She has been working on Ideal Convergence of double sequence. She has published 17 research papers in National and International Journals including Cogent Mathematics, Filomat, Journal of Intelligent and Fuzzy Systems, Advances in Difference Equations, Journal of Mathematical Analysis, Journal of Mathematical & Computer Science etc. She has also reviewed few research papers for the and international journals. She is a member of Indian Mathematical Society.",institutionString:null,institution:null},{id:"414880",title:"Dr.",name:"Maryam",middleName:null,surname:"Vatankhah",slug:"maryam-vatankhah",fullName:"Maryam Vatankhah",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Borough of Manhattan Community College",country:{name:"United States of America"}}},{id:"414879",title:"Prof.",name:"Mohammad-Reza",middleName:null,surname:"Akbarzadeh-Totonchi",slug:"mohammad-reza-akbarzadeh-totonchi",fullName:"Mohammad-Reza Akbarzadeh-Totonchi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Ferdowsi University of Mashhad",country:{name:"Iran"}}},{id:"414878",title:"Prof.",name:"Reza",middleName:null,surname:"Fazel-Rezai",slug:"reza-fazel-rezai",fullName:"Reza Fazel-Rezai",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"American Public University System",country:{name:"United States of America"}}},{id:"426586",title:"Dr.",name:"Oladunni A.",middleName:null,surname:"Daramola",slug:"oladunni-a.-daramola",fullName:"Oladunni A. Daramola",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Federal University of Technology",country:{name:"Nigeria"}}},{id:"357014",title:"Prof.",name:"Leon",middleName:null,surname:"Bobrowski",slug:"leon-bobrowski",fullName:"Leon Bobrowski",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Bialystok University of Technology",country:{name:"Poland"}}},{id:"302698",title:"Dr.",name:"Yao",middleName:null,surname:"Shan",slug:"yao-shan",fullName:"Yao Shan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Dalian University of Technology",country:{name:"China"}}},{id:"354126",title:"Dr.",name:"Setiawan",middleName:null,surname:"Hadi",slug:"setiawan-hadi",fullName:"Setiawan Hadi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Padjadjaran University",country:{name:"Indonesia"}}},{id:"125911",title:"Prof.",name:"Jia-Ching",middleName:null,surname:"Wang",slug:"jia-ching-wang",fullName:"Jia-Ching Wang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"National Central University",country:{name:"Taiwan"}}},{id:"332603",title:"Prof.",name:"Kumar S.",middleName:null,surname:"Ray",slug:"kumar-s.-ray",fullName:"Kumar S. Ray",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Statistical Institute",country:{name:"India"}}},{id:"415409",title:"Prof.",name:"Maghsoud",middleName:null,surname:"Amiri",slug:"maghsoud-amiri",fullName:"Maghsoud Amiri",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Allameh Tabataba'i University",country:{name:"Iran"}}},{id:"357085",title:"Mr.",name:"P. Mohan",middleName:null,surname:"Anand",slug:"p.-mohan-anand",fullName:"P. Mohan Anand",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"356696",title:"Ph.D. Student",name:"P.V.",middleName:null,surname:"Sai Charan",slug:"p.v.-sai-charan",fullName:"P.V. Sai Charan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"357086",title:"Prof.",name:"Sandeep K.",middleName:null,surname:"Shukla",slug:"sandeep-k.-shukla",fullName:"Sandeep K. Shukla",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}}]}},subseries:{item:{id:"18",type:"subseries",title:"Proteomics",keywords:"Mono- and Two-Dimensional Gel Electrophoresis (1-and 2-DE), Liquid Chromatography (LC), Mass Spectrometry/Tandem Mass Spectrometry (MS; MS/MS), Proteins",scope:"With the recognition that the human genome cannot provide answers to the etiology of a disorder, changes in the proteins expressed by a genome became a focus in research. Thus proteomics, an area of research that detects all protein forms expressed in an organism, including splice isoforms and post-translational modifications, is more suitable than genomics for a comprehensive understanding of the biochemical processes that govern life. The most common proteomics applications are currently in the clinical field for the identification, in a variety of biological matrices, of biomarkers for diagnosis and therapeutic intervention of disorders. From the comparison of proteomic profiles of control and disease or different physiological states, which may emerge, changes in protein expression can provide new insights into the roles played by some proteins in human pathologies. Understanding how proteins function and interact with each other is another goal of proteomics that makes this approach even more intriguing. Specialized technology and expertise are required to assess the proteome of any biological sample. Currently, proteomics relies mainly on mass spectrometry (MS) combined with electrophoretic (1 or 2-DE-MS) and/or chromatographic techniques (LC-MS/MS). MS is an excellent tool that has gained popularity in proteomics because of its ability to gather a complex body of information such as cataloging protein expression, identifying protein modification sites, and defining protein interactions. The Proteomics topic aims to attract contributions on all aspects of MS-based proteomics that, by pushing the boundaries of MS capabilities, may address biological problems that have not been resolved yet.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/18.jpg",hasOnlineFirst:!0,hasPublishedBooks:!0,annualVolume:11414,editor:{id:"200689",title:"Prof.",name:"Paolo",middleName:null,surname:"Iadarola",slug:"paolo-iadarola",fullName:"Paolo Iadarola",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSCl8QAG/Profile_Picture_1623568118342",biography:"Paolo Iadarola graduated with a degree in Chemistry from the University of Pavia (Italy) in July 1972. He then worked as an Assistant Professor at the Faculty of Science of the same University until 1984. In 1985, Prof. Iadarola became Associate Professor at the Department of Biology and Biotechnologies of the University of Pavia and retired in October 2017. Since then, he has been working as an Adjunct Professor in the same Department at the University of Pavia. His research activity during the first years was primarily focused on the purification and structural characterization of enzymes from animal and plant sources. During this period, Prof. Iadarola familiarized himself with the conventional techniques used in column chromatography, spectrophotometry, manual Edman degradation, and electrophoresis). Since 1995, he has been working on: i) the determination in biological fluids (serum, urine, bronchoalveolar lavage, sputum) of proteolytic activities involved in the degradation processes of connective tissue matrix, and ii) on the identification of biological markers of lung diseases. In this context, he has developed and validated new methodologies (e.g., Capillary Electrophoresis coupled to Laser-Induced Fluorescence, CE-LIF) whose application enabled him to determine both the amounts of biochemical markers (Desmosines) in urine/serum of patients affected by Chronic Obstructive Pulmonary Disease (COPD) and the activity of proteolytic enzymes (Human Neutrophil Elastase, Cathepsin G, Pseudomonas aeruginosa elastase) in sputa of these patients. More recently, Prof. Iadarola was involved in developing techniques such as two-dimensional electrophoresis coupled to liquid chromatography/mass spectrometry (2DE-LC/MS) for the proteomic analysis of biological fluids aimed at the identification of potential biomarkers of different lung diseases. He is the author of about 150 publications (According to Scopus: H-Index: 23; Total citations: 1568- According to WOS: H-Index: 20; Total Citations: 1296) of peer-reviewed international journals. He is a Consultant Reviewer for several journals, including the Journal of Chromatography A, Journal of Chromatography B, Plos ONE, Proteomes, International Journal of Molecular Science, Biotech, Electrophoresis, and others. He is also Associate Editor of Biotech.",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorTwo:{id:"201414",title:"Dr.",name:"Simona",middleName:null,surname:"Viglio",slug:"simona-viglio",fullName:"Simona Viglio",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRKDHQA4/Profile_Picture_1630402531487",biography:"Simona Viglio is an Associate Professor of Biochemistry at the Department of Molecular Medicine at the University of Pavia. She has been working since 1995 on the determination of proteolytic enzymes involved in the degradation process of connective tissue matrix and on the identification of biological markers of lung diseases. She gained considerable experience in developing and validating new methodologies whose applications allowed her to determine both the amount of biomarkers (Desmosine and Isodesmosine) in the urine of patients affected by COPD, and the activity of proteolytic enzymes (HNE, Cathepsin G, Pseudomonas aeruginosa elastase) in the sputa of these patients. Simona Viglio was also involved in research dealing with the supplementation of amino acids in patients with brain injury and chronic heart failure. She is presently engaged in the development of 2-DE and LC-MS techniques for the study of proteomics in biological fluids. The aim of this research is the identification of potential biomarkers of lung diseases. She is an author of about 90 publications (According to Scopus: H-Index: 23; According to WOS: H-Index: 20) on peer-reviewed journals, a member of the “Società Italiana di Biochimica e Biologia Molecolare,“ and a Consultant Reviewer for International Journal of Molecular Science, Journal of Chromatography A, COPD, Plos ONE and Nutritional Neuroscience.",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorThree:null,series:{id:"11",title:"Biochemistry",doi:"10.5772/intechopen.72877",issn:"2632-0983"},editorialBoard:[{id:"72288",title:"Dr.",name:"Arli Aditya",middleName:null,surname:"Parikesit",slug:"arli-aditya-parikesit",fullName:"Arli Aditya Parikesit",profilePictureURL:"https://mts.intechopen.com/storage/users/72288/images/system/72288.jpg",institutionString:null,institution:{name:"Indonesia International Institute for Life 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